How predictive modelling and optimization can maximize recovered amounts with a focus on Next Best Action assignment. In 2023, the US economy outperformed expectations, with strong job growth, impressive GDP (annual growth rate was 2.5%, up from 1.9% in 2022), and lower inflation. Increased consumer spending and reduced trade deficit highlighted its resilience and adaptability, fostering a stable economic environment. However, the story around consumer debt and delinquencies has not been so positive. In the latest quarterly report on household debt and credit released in February 2024 by the Federal Reserve Bank of New York, total household debt saw a notable increase of $212 billion (1.2%) in the fourth quarter of 2023, reaching $17.5 trillion. Within this surge, credit card balances increased by $50 billion, alongside mortgage balances which rose by $112 billion to hit $12.12 trillion. Auto loans, which have been trending upwards since 2011, saw an additional $12 billion increase, totalling $1.61 trillion. Other balances, encompassing retail cards and various consumer loans, witnessed a growth of $25 billion. Despite the economic recovery post-Covid, the level of debt in credit cards and auto loans, transitioning into delinquency remains higher than pre-pandemic levels. In Q4 2023, aggregated delinquency rates reached 3.1%, signifying a persistent financial strain for many lower income households. Transition rates into delinquency increased across all debt categories except for student loans. Approximately 8.5% of credit card balances and 7.1% of auto loans transitioned into delinquency on an annualised basis. Serious credit card delinquencies (90 days +) surged across all age groups, especially among younger borrowers, surpassing pre-pandemic levels. With such elevated debt and early-stage delinquency rates, lenders face many challenges. We look at how predictive modelling and optimization can maximize recovered amounts with a focus on Next Best Action assignment. Collections managers and teams within financial institutions face a range of challenges in maintaining portfolio growth while effectively managing increases in early-stage delinquencies. The top five challenges include: 1. High operating costs Contacting delinquent customers, negotiating payments, and managing recovery efforts entail labor-intensive and costly processes. This encompasses expenses related to staffing call centres, sending mailers, and deploying collections management software. 2. Regulatory compliance Navigating federal, state, and local regulations governing debt collection practices presents a complex challenge. Compliance with laws such as the Fair Debt Collection Practices Act (FDCPA) and the Telephone Consumer Protection Act (TCPA) is imperative, dictating the permissible methods and timing of borrower contact. 3. Customer retention and satisfaction Balancing effective debt recovery with maintaining positive customer relationships is essential. Employing aggressive collection tactics risks damaging customer relationships and tarnishing brand reputation, potentially impacting long-term customer retention. 4. Technological integration Incorporating modern technologies like machine learning, and automation into the collections process can enhance efficiency but poses implementation challenges. These technologies require substantial investment and expertise to streamline operations effectively. 5. Data management and predictive analytics Efficiently managing and analyzing vast amounts of data to identify at-risk accounts early and customise collection strategies is a significant endeavour. Accurate data analysis is pivotal for predicting delinquencies likely to self-cure and determining appropriate contact channels, such as; SMS, Email, Phone, Outbound IVR or social media. Applying a customer-centric, strategic approach These challenges underscore the critical need for credit lenders to adopt strategic, compliant, and customer-centric approaches to early-stage delinquency management. Currently, financial institutions use a multitude of strategies to maximize revenue collection. These range from data-driven customer segmentation to profile customers, Regulatory Technology (RegTech) for compliance, proactively identifying vulnerable customers needing financial relief, offering flexible repayment solutions and predictive modelling. Some credit lenders are also using machine learning models, such as Next Best Action (NBA) to personalize collection strategies based on customer behaviour, financial status, and communication preferences. This approach predicts recovery rates by tailoring channel contact to each individual customer in the most effective way. However, NBA models alone are not enough. To maximise collections, within known business constraints (call centre resources, budget, regulations), NBA needs to be augmented with non-linear optimization techniques to ensure not only the right communication preferences are adhered to, but also the business constraints mentioned above. Without the optimisation component businesses are left with NBA modelling that is unadjusted for business constraints. Next Best Action (NBA) Optimization NBA optimization presents a game-changing opportunity for lenders, particularly given the current economic challenges consumers are facing. Here's how NBA optimization can drive value: Personalized communication NBA optimization uses sophisticated customer modelling to pinpoint the most effective communication channels for each borrower, be it email, text, phone, or another preferred method. By personalizing communications, lenders significantly increase the chance of response and engagement from customers, which will also streamline the collections process with greater efficiency and reduced intrusion. Dynamic strategy adjustment NBA solutions continuously learn from outcomes, enabling strategy adjustments. This dynamic capability empowers lenders to swiftly adapt to changing economic conditions, borrower behaviours, and regulatory landscapes, ensuring the maintenance of effective collections practices. Optimized timing Leveraging predictive modelling, NBA optimization empowers lenders to identify the best times to contact their customers. This strategic approach ensures their communication attempts yield higher success rates, minimizing the need for repeated contacts and reducing operational costs. Regulatory compliance NBA optimization solutions can be configured to seamlessly adhere to regulatory requirements, including permissible contact times and frequency limits. This automation ensures compliance, protecting lenders from legal penalties and upholding their standing with regulatory bodies. Operational efficiency Through automated decision-making processes, NBA optimization assists lenders in allocating resources more thoughtfully. By prioritizing accounts with higher payment probabilities and determining the most cost-effective collection strategies, lenders can streamline operations and minimize costs. Improved customer experience (CX) NBA optimization facilitates a tailored approach to debt collection, significantly enhancing the borrower's experience. By considering the borrower's unique circumstances and preferences, lenders can offer more relevant and flexible repayment options, while also boosting customer satisfaction and loyalty. By implementing NBA optimization customised to channel contact preferences and operational constraints, lenders can navigate the complexities of early-stage collections with precision. This strategic approach not only addresses operational challenges but also aligns with the evolving expectations and financial pressures of consumers, leading to improved outcomes for both lenders and borrowers. Businesses can assign the most profitable, cost-effective treatment and channel to contact customers. Ascend Intelligence Services™ Collect delivers an optimized collections decision strategy, driven by predictive analytics, that determines the next best action and contact channel for each individual customer to improve recovery rates, increase efficiency, and stay within day-to-day constraints and regulatory requirements. Find out more

New IDC MarketScape: Worldwide Enterprise Fraud Solutions 2024 Vendor assessment provides valuable resource as organizations face increased fraud. With fraud scam losses reported to have reached $10bn in 2023*, preventing fraud in today's digital landscape has become increasingly complex. As organizations continue to leverage advanced technologies, fraudsters have also evolved, employing ever more sophisticated techniques. Striking the balance between robust fraud prevention and delivering a seamless digital experience to customers has become a priority for organizations, with customer experience (CX) proving to be a competitive differentiator in a market with high digital expectations. Why real-time detection matters for CX As techniques employed by fraudsters get faster, so does the need for quick and effective fraud detection, making real-time solutions increasingly important during a period of rapid technological advancement. The development of real-time fraud solutions not only minimizes financial losses, but it has also paved the way for frictionless customer journeys, with identity and fraud checks no longer impeding customer experience. Using machine learning to leverage data and enable fraud detection To enable real-time detection, proactive fraud prevention also requires the analysis of vast amounts of data. Deploying static rules to identify anomalies in data does not allow for nuance because the thresholds within the rules are fixed, and therefore real-time patterns cannot be adjusted to within the model. Machine learning not only allows businesses to leverage data more effectively through analysis, allowing for flexibility within the parameters, but it also removes some manual processes, improving efficiency by updating models faster into production. Approving good customers is the number one priority for businesses, and a frictionless digital customer journey is the catalyst for this. To minimize financial losses while reducing the overall number of fraud incidents, organizations are looking to real-time fraud detection, enabled by machine learning. "As fraud risk losses continue to increase, the pursuit of fraud risk management solutions designed to identify, mitigate, and prevent fraud incidents and losses is a topic with increasing focus within financial services.” Sean O'Malley, research director, IDC Financial Insights: Worldwide Compliance, Fraud and Risk Analytics Strategies IDC, the premier global market intelligence firm, released its latest IDC MarketScape: Worldwide Enterprise Fraud Solutions, providing a valuable resource to buyers looking for new solutions in the market. Download excerpt of IDC MarketScape: Worldwide Enterprise Fraud Solutions 2024 Vendor Assessment The report highlights: Fraud solutions are increasingly moving toward real-time fraud detection and prevention. There are significant enhancements in technological capabilities, particularly with respect to cloud computing. Some newer fraud solutions take advantage of the increased computing power that is available to both expand the data sets being used to identify potential fraud incidents and enhance the models designed to detect, mitigate, and prevent fraud. Experian is recognized as a leader in this report. The IDC MarketScape notes, “In addition to evaluating the transactional data for potential fraud, Experian's CrossCore solution includes identity-authentication tools. The solution uses identity data, device intelligence, email and phone intelligence, alternative identity data, biometrics, behavioral biometrics, one-time passwords, and document verification to confirm identities and aid with identity protection, including synthetic identity protection. Experian utilizes multiple data partnerships in its fraud solution, which often can help provide a more comprehensive understanding of fraud risks and exposures.” To achieve a frictionless and secure customer experience, it is the integration of digital identity and fraud risk that is creating a gold standard for businesses. A siloed approach to fraud prevention not only leaves gaps for criminals to exploit, but it also presents consequences for customer experience too. The ability to layer multiple fraud capabilities together in a synchronized effort to achieve the best analytics-driven output possible can allow businesses to have the flexibility within their user journeys to optimize and control the order in which capabilities are called, removing friction, and ensuring good customers are successfully onboarded. Add in a final layer of machine learning to ensure the deployment of unified decisioning, and businesses are left with cohesive and explainable decisions. At Experian, we are working diligently to stay on the cutting edge of fraud and identity. In addition to our proprietary credit data on over 1.5 billion consumers and over 200 million businesses, Experian leverages a unique curated partner ecosystem to provide a more comprehensive understanding of fraud risks and exposures. Our powerful technology platform enables users to leverage a wide range of tools to combat their customized fraud challenges. Download Report Excerpt More on Crosscore® *IDC MarketScape: Worldwide Enterprise Fraud Solutions 2024 Vendor Assessment

With the potential annual value of AI and analytics for global banking estimated to reach $1 trillion,1 financial institutions are seeking out efficient ways to implement insights-driven lending. As regulators continue to supervise risk management, lenders must balance the opportunity presented by AI to determine risk more accurately while growing approval rates and reducing the cost of acquisition, with the ability to explain decisions. The challenge of using AI in building credit risk models In a recent study conducted by Forrester Consulting on behalf of Experian, the top pain points for technology decision makers in financial services were reported to be automation and availability of data.2 The implementation of accessible AI solutions in credit risk management allows businesses to improve efficiency and time-to-market metrics by widening data sources, improving automation and decreasing risk. But the implementation of AI and machine learning in credit risk models can pose other challenges. The study also found that 31% of respondents felt that their organization could not clearly explain the reasoning behind credit decisions to customers.2 Although AI has been proven to improve the accuracy of predictive credit risk models, these advancements mean that many organizations need support in understanding and explaining the outcomes of AI-powered decisions to fulfil regulatory obligations, such as the Equal Credit Opportunity Act (ECOA). Moving from traditional model development methodologies to Machine Learning (ML) As lenders move away from traditional parametric models like logistic regression, to ML models like neural nets or tree-based ensemble methods, explainability becomes more complex. Logistic regression has for many years allowed for a clear understanding of the linear relationships between model attributes and the outcome (approval or decline). Once the model is estimated, it is completely explainable. However, ML models are non-parametric, so there are no underlying assumptions made around the distribution (shape) of the sample. Furthermore, the relationships between attributes and outcomes are not assumed to be linear – they’re often non-linear and complex, involving interactions. Such models are perceived to be black boxes where data is consumed as an input, processed and a decision is made without any visibility around the inner dynamics of the model. At the same time, it is possible for ML models to perform better when accurately classifying good customers and those deemed delinquent. Ensuring transparency and explainability is crucial – lenders must be able to identify and explain the most dominant attributes that contribute towards a decision to lend or not. They must also provide ‘reason codes’ at the customer level so any declined applicants can fully understand the main cause and have a path to remediation. The importance of developing transparent and explainable models By prioritizing the development of transparent and interpretable models, financial institutions can also better foster equitable lending practices. However, fair credit decisioning goes beyond the regulatory and ethical obligations - it also makes business sense. Unfair lending leads to higher default rates if creditworthiness is not accurately assessed, therefore increasing bad debts. Removing demographics considered to be the ‘unscored’ or ‘underserved’ (those who are credit worthy but do not have a traditional data trail, but instead a digital footprint comprised of alternative data) can also limit portfolio opportunity for businesses. For these reasons, it is critical to remove or minimize model bias. Bias is an upstream issue that starts at the data collection stage and model algorithm selections. Models developed using logistic regression or machine learning algorithms can be made fairer through carefully selecting attributes relevant to credit decisioning and avoiding sensitive attributes like race, gender, or ethnicity. Wherever sensitive metrics are used, they should be down-weighted to suppress their impact on lending decisions. Some other techniques to mitigate bias include: Thoroughly reviewing the data samples used in modelling. Fair Model Training - Train models using fairness-aware techniques. This may involve adjusting the training process to penalise any discrimination that creeps in. According to Forrester, an essential component of a decisioning platform is one that can “harness the power of AI while enhancing and governing it with well-proven and trusted human business expertise. The best automated decisions come from a combination of both.”3 Developing explainable models goes some way towards reducing bias, but making the decisions explainable to regulatory bodies is a separate issue, and in the digital age of AI, can require deep domain expertise to fulfil. While AI-powered decisioning can help businesses make smarter decisions, they also need the ability to confidently explain their lending practices to stay compliant. With the help of an expert partner, organizations can gain an understanding of what contributed most to a decision and receive detailed and transparent documentation for use with regulators. This ensures lenders can safely grow approval rates, be more inclusive, and better serve their customers. “The solution isn’t simply finding better ways to convey how a system works; rather, it’s about creating tools and processes that can help even the deep expert understand the outcome and then explain it to others.”McKinsey: why businesses need explainable ai and how to deliver it Experian’s Ascend Intelligence ServicesTM Acquire is a custom credit risk model development service that can better quantify risk, score more applicants, increase automation, and drive more profitable decisions. Find out more Confidently explain lending practices:Detailed, rigorous, and transparent documentation that has been proven to meet the strictest regulatory standards. Breaking Machine Learning (ML) out of the black box:Understand what contributed most to a decision and generate adverse action codes directly from the model through our patent-pending ML explainability.References: "The executive's AI playbook," McKinsey.com. (See "Banking," under "Value & Assess.") In a study conducted by Forrester Consulting on behalf of Experian, we surveyed 660 and interviewed 60 decision makers for technology purchases that support the credit lifecycle at their financial services organisation. The study included businesses across North America, UK and Ireland, and Brazil. 2023_05_Forrester_AI-Decisioning-Platforms-Wave.pdf https://www.mckinsey.com/capabilities/quantumblack/our-insights/why-businesses-need-explainable-ai-and-how-to-deliver-it Contributors:Masood Akhtar, Global Product Marketing Manager

We explore four fraud trends likely to be influenced the most by GEN AI technology in 2024, and what businesses can do to prevent them. 2023: The rise of Generative AI 2023 was marked by the rise of Generative Artificial Intelligence (GEN AI), with the technology’s impact (and potential impact) reverberating across businesses around the world. 2023 also witnessed the democratisation of GEN AI, with its usage made publicly available through multiple apps and tools such as Open AI's Chat GPT and DALL·E, Google's Bard, Midjourney, and many others. Chat GPT even held the world record for the fastest growing application in history (until it was surpassed by Threads) after reaching 100 million users in January 2023, just less than 2 months after its launch. The profound impact of GEN AI on everyday life is also reflected in the 2023 Word of the Year (WOTY) lists published by some of the biggest dictionaries in the world. Merriam-Webster’s WOTY for 2023 was 'authentic'— a term that people are thinking about, writing about, aspiring to, and judging more than ever. It's also not a surprise that one of the other words outlined by the dictionary was 'deepfake', referencing the importance of GEN AI-inspired technology over the past 12 months. Among other dictionaries that publish WOTY lists, both Cambridge Dictionary and Dictionary.com chose 'hallucinate' - with new definitions of the verb describing false information produced by AI tools being presented as truth or fact. A finalist in the Oxford list was the word 'prompt', referencing the instructions that are given to AI algorithms to influence the content it generates. Finally, Collins English Dictionary announced 'AI' as their WOTY to illustrate the significance of the technology throughout 2023. GEN AI has many potential positive applications from streamlining business processes, providing creative support for various industries such as architecture, design, or entertainment, to significantly impacting healthcare or education. However, as signalled out by some of the WOTY lists, it also poses many risks. One of the biggest threats is its adoption by criminals to generate synthetic content that has the potential to deceive businesses and individuals. Unfortunately, easy-to-use, and widely available GEN AI tools have also created a low entrance point for those willing to commit illegal activities. Threat actors leverage GEN AI to produce convincing deepfakes that include audio, images, and videos that are increasingly sophisticated and practically impossible to differentiate from genuine content without the help of technology. They are also exploiting the power of Large Language Models (LLMs) by creating eloquent chatbots and elaborate phishing emails to help them steal important information or establish initial communication with their targets. GEN AI fraud trends to watch out for in 2024 As the lines between authentic and synthetic blur more than ever before, here are four fraud trends likely to be influenced most by GEN AI technology in 2024. A staggering rise in bogus accounts: (impacted by: deepfakes, synthetic PII)Account opening channels will continue to be impacted heavily by the adoption of GEN AI. As criminals try to establish presence in social media and across business channels (e.g., LinkedIn) in an effort to build trust and credibility to carry out further fraudulent attempts, this threat will expand way beyond the financial services industry. GEN AI technology continues to evolve, and with the imminent emergence of highly convincing real-time audio and video deepfakes, it will give fraudsters even better tools to attempt to bypass document verification systems, biometric and liveness checks. Additionally, they could scale their registration attempts by generating synthetic PII data such as names, addresses, emails, or national identification numbers. Persistent account takeover attempts carried out through a variety of channels: (impacted by: deepfakes, GEN AI generated phishing emails)The advancements in deepfakes present a big challenge to institutions with inferior authentication defenses. Just like with the account opening channel, fraudsters will take advantage of new developments in deepfake technology to try to spoof authentication systems with voice, images, or video deepfakes, depending on the required input form to gain access to an account. Furthermore, criminals could also try to fool customer support teams to help them regain access they claim to have lost. Finally, it's likely that the biggest threat would be impersonation attempts (e.g., criminals pretending to be representatives of financial institutions or law enforcement) carried out against individuals to try to steal access details directly from them. This could also involve the use of sophisticated GEN AI generated emails that look like they are coming from authentic sources. An influx of increasingly sophisticated Authorised Push Payment fraud attempts: (impacted by: deepfakes, GEN AI chatbots, GEN AI generated phishing emails)Committing social engineering scams has never been easier. Recent advancements in GEN AI have given threat actors a handful of new ways to deceive their victims. They can now leverage deepfake voices, images, and videos to be used in crimes such as romance scams, impersonation scams, investment scams, CEO fraud, or pig butchering scams. Unfortunately, deepfake technology can be applied to multiple situations where a form of genuine human interaction might be needed to support the authenticity of the criminals' claims. Fraudsters can also bolster their cons with GEN AI enabled chatbots to engage potential victims and gain their trust. If that isn’t enough, phishing messages have been elevated to new heights with the help of LLM tools that have helped with translations, grammar, and punctuation, making these emails look more elaborate and trustworthy than ever before. A whole new world of GEN AI Synthetic Identity: (impacted by: deepfakes, synthetic PII)This is perhaps the biggest fraud threat that could impact financial institutions for years to come. GEN AI has made the creation of synthetic identities easier and more convincing than ever before. GEN AI tools give fraudsters the ability to generate fake PII data at scale with just a few prompts. Furthermore, criminals can leverage fabricated deepfake images of people that never existed to create synthetic identities from entirely bogus content. Unfortunately, since synthetic identities take time to be discovered and are often wrongly classified as defaults, the effect of GEN AI on this type of fraud will be felt for a long time. How to prevent GEN AI related fraud As GEN AI technology continues to evolve in 2024, its adoption by fraud perpetrators to carry out illegal activities will too. Institutions should be aware of the dangers they possess and equip themselves with the right tools and processes to tackle these risks. Here are a few suggestions on how this can be achieved: Fight GEN AI with GEN AI: One of the biggest advantages of GEN AI is that while it is being trained to create synthetic data, it can also be trained to spot it successfully. One such approach is supported by Generative Adversarial Networks (GANs) that employ two neural networks competing against each other — a generator and a discriminator. The generator creates synthetic data, while the discriminator evaluates the generated data and tries to distinguish between real and fake samples. Over time, both networks fine tune themselves, and the discriminator becomes increasingly successful in recognising synthetic content. Other algorithms used to create deepfakes, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders, can also be trained to spot anomalies in audio, images, and video, such as inconsistencies in facial movements or features, inconsistencies in lighting or background, unnatural movements or flickering, and audio discrepancies. Finally, a hybrid approach that combines multiple algorithms often presents more robust results. Advanced analytics to monitor the whole customer journey and beyond: Institutions should deploy a fraud solution that leverages data from a variety of tools that can spot irregular activity across the whole customer journey. That could be a risky activity, such as a spike in suspicious registrations or authentication attempts, unusual consumer behaviour, irregular login locations, suspicious device or browser data, or abnormal transaction activity. A best-in-class solution would give institutions the ability to monitor and analyse trends that go beyond a single transaction or account. Ideally, that means monitoring for fraud signals happening both within a financial institution’s environment and across the industry. This should allow businesses to discover signals pointing out fraudulent activity previously not seen within their systems or data points that would otherwise be considered safe, thus allowing them to develop new fraud prevention models and more comprehensive strategies. Fraud data sharing: Sharing of fraud data across multiple organisations can help identify and spot new fraud trends from occurring within an instruction's premises and stop risky transactions early. Educate consumers: While institutions can deploy multiple tools to monitor GEN AI related fraud, regular consumers don't have the same advantage and are particularly susceptible to impersonation attempts, among other deepfake or GEN AI related cons. While they can't be equipped with the right tools to recognize synthetic content, educating consumers on how to react in certain situations related to giving out valuable personal or financial information is an important step in helping them to remain con free. Learn more with our latest fraud reports from across the globe: UK Fraud Report 2023 US Fraud Report 2023 EMEA + APAC Fraud Report 2023

Lenders prioritise automation above all, according to research. In a study conducted by Forrester Consulting on behalf of Experian, we surveyed 660 and interviewed 60 decision makers for technology purchases that support the credit lifecycle at their financial services organisation. The study included businesses across North America, UK and Ireland, and Brazil. Research from Forrester on behalf of Experian found that automation is the top priority for businesses, and regardless of the specific industry or region, decision-makers consistently identified it as an important area of focus, and the biggest challenge. Lenders are using automation across the credit lifecycle and intend to invest further in the next 12 months, but there are multiple barriers to enhancing automation. We look at the use cases for automation and address the key challenges lenders face when automating decisions. The automation agenda The interpretation and application of automation vary hugely across the maturity spectrum of businesses in our research. While some companies consider automation as a means of simplifying tasks, such as the transition from manual processes to electronic spreadsheets, others are embracing its more advanced forms, such as AI-powered models. Use cases for automation in lending Customer service chatbots using Natural Language Processing (NPL) combined with Robotic Process Automation system (RPA). Remote verification of customers using machine vision and RPA to cross-check data. Data governance - data cleansing of personal information from within data using RPA and NPL. Operational efficiencies using process mining and AI to identify automation opportunities. Credit and fraud risk decisioning, using machine learning. Automation is about making processes as slick and robust as possible, giving the consumer a rapid journey so they can get processed very quickly, while behind the scenes lenders are making the best possible, compliant decisions, that protect them from losses around both credit risk and fraud.Neil Stephenson, Vice President of Experian SOFTWARE SOLUTIONS CONSULTANCY The changing face of automated decision-making in line with rapid tech advancements makes the use of automation by lenders a more complex opportunity than most. On one side there is the chance to enhance models with AI-powered tools to take away manual and subjective decision-making from processes. On the other, there’s the issue of governance and compliance – how to explain models that remove humans altogether. Introducing automation into some parts of the credit lifecycle isn’t always straightforward. Customer management has benefited from a lot of investment in the automation space over the years, particularly Natural Language Processing (NLP), but according to our research, the priority for business investment for Robotic Process Automation (RPA) in the next 12 months is originations. With onboarding playing such a key role in both customer experience and portfolio growth, businesses are looking to enhance this part of the credit lifecycle with automation. Customer experience is driving growth Automation plays a pivotal role in improving the customer journey and experience. The research showed that enhancing customer experience ranked even higher than growth as a priority for many organisations. As businesses strive to deliver seamless and personalised interactions, automation provides the necessary foundation for digital success, which in turn can strengthen competitiveness while retaining valuable customers. "Strategically investing in automation offers businesses the opportunity to scale operations, with a primary focus on growth. In times of economic uncertainty, more targeted, customer-centric strategies, that encompass more accurate predictive models, built on up-to-date samples and executed rapidly, can help mitigate a higher-risk lending backdrop." says Neil Stephenson, Vice President of Experian Software Solutions Consultancy. "Customer experience is the battleground for businesses, where they compete to deliver the best digital journeys in the market. It's a battleground that isn’t just about increasing revenue – the market perception of an organisation can be as important as growth in some portfolios because businesses have a reputation to protect." Automating decisions can ensure customer experience is truly seamless, but businesses face multiple barriers when it comes to credit and risk decision automation. Reducing referred applications  From scoring regression models to the development of machine learning models, better and smarter analytics are critical to drive the processes responsible for making application decisions. Reducing referred applications in turn decreases the need for manual intervention. By minimising the volume of applications in the middle of the credit score, lenders have a clearer and ultimately more automated approach to application accepts and declines. We interviewed decision-makers to understand the numerous challenges faced by lenders when automating decisions: Increasing data sources to allow for a more complete picture of the consumer Improved data quality, and increased volume of data Prevention of model bias The complexity of consumer type attached to some products Redundancy in data input and analytics Training across key roles for a better understanding of automation capabilities Explaining decisions based on machine learning models to regulators Complex fraud referral processes For many respondents, automation is about accuracy and efficiency. By improving automation, there are fewer instances of errors and delays. To ensure scalability can exist in consistent, compliant, and accurate processes that work for both the business and the consumer, here are 10 tips to help tackle the challenges faced by lenders when it comes to automating decisions: Embrace advanced data aggregation tools and technologies that can efficiently collect and integrate data from various sources. Partner with known, trustworthy data providers to enrich datasets. Explore the use of no-code data management tools that allow users to add and remove data sources more quickly and easily. Implement data quality processes. Regularly audit and clean data to remove inconsistencies. Move to cloud-based solutions for scalable data storage and processing of very large datasets. Regularly audit (monitor) machine learning models for bias.  Eliminating sampling bias is not yet possible but using a range of datasets (samples) and various sampling techniques will ensure representation across different demographics to help minimise bias. Develop specific models for different consumer segments or product categories. Regularly update models based on evolving consumer trends and behaviours. Conduct a thorough analysis of data inputs and streamline redundant variables. Use feature selection techniques such as correlations, weight of evidence, and information value to identify the most relevant information. Foster a culture of continuous learning and collaboration for all key stakeholders involved in the credit decisioning and strategy process. Develop transparent models with explainable features. Use interpretable machine learning algorithms that allow for clear explanations of decision factors at the customer level. Streamline identity verification processes by using smart orchestration to reduce false positives and prevent fraud. More on automated decision-making from PowerCurve – North America More on automated decision-making from PowerCurve – UK Related content: Digital decisioning

Authorised Push Payment fraud is growing, and as regulators begin to take action around the world to try to tackle it, we look at what financial institutions need to focus on now. APP fraud and social engineering scams In recent years, there has been a significant surge in reported instances of Authorized Push Payment Fraud (APP). These crimes, also known as financial scams, wire fraud scams, or social engineering scams in different parts of the world, refer to a type of fraud where criminals trick victims into authorising a payment to an account controlled by the fraud perpetrator for what the victim believes to be genuine goods or services in return for their money. Because the transactions made by the victim are usually done using a real-time payment scheme, they are often irrevocable. Once the fraudster receives the funds, they are quickly transferred through a series of mule accounts and withdrawn, often abroad. Because APP fraud often involves social engineering, it employs some of the oldest tricks in the criminal's book. These scams include tactics such as applying pressure on victims to make quick decisions, or enticing them with too-good-to-be-true schemes and tempting opportunities to make a fortune. Unfortunately, these tricks are also some of the most successful ones, and criminals have used them to their advantage more than ever in recent times. On top of that, with the widespread adoption of real-time payments, victims have the ability to transfer funds quickly and easily, making it much easier for criminals to take advantage of the process. APP Fraud and social engineering scams - cases and losses across the globe: View map Impact of AI on APP fraud Recent advancements in generative artificial intelligence (Gen AI) have accelerated the process used by fraudsters in APP fraud. Criminals use apps like Chat GPT and Bard to create more persuasive messages, or bot functionality offered by Large Language Models (LLMs) to engage their victims into romance scams and the more sophisticated pig butchering scams. Other examples include the use of face swapping apps or audio and video deepfakes that help fraudsters impersonate someone known to their victims, or create a fictitious personality that they believe to be a real person. Additionally, deepfake videos of celebrities have also been commonly used to trick victims into making an authorised transaction and lose substantial amounts of money. Unfortunately, while some of these hoaxes were really difficult to pull off a few years ago, the widespread availability of easy-to-use Gen AI technology tools has resulted in an increased number of attacks. A lot of these scams can be traced back to social media, where the initial communication between the victim and criminal takes place. According to UK Finance, 78% of APP fraud started online during the second half of 2022, and this figure was similar for the first half of 2023 at 77%. Fraudsters also use social media to research their victims which makes these attacks highly personalised due to the availability of data about potential targets. Accessible information often includes facts related to family members, things of personal significance like hobbies or spending habits, information about favourite holiday destinations, political views, or random facts like favourite foods and drink. On top of that, criminals use social media to gather photos and videos of potential targets or their family members that can later be leveraged to generate convincing deepfake content that includes audio, video, or images. These things combined contribute to a new, highly personalised approach to scams than has never been seen before. What regulators are saying around the globe APP fraud mitigation is a complex task that requires collaboration by multiple entities. The UK is by far the most advanced jurisdiction in terms of measures taken to tackle these types of fraud to help protect consumers. Some of the most important legislative changes that the UK’s Payment Systems Regulator (PSR) has proposed or introduced so far include: Mandatory reimbursement of APP scams victims: A world first mandatory reimbursement model will be introduced in 2024 to replace the previous voluntary reimbursement code which has been operational since 2019. 50/50 liability split: All payment firms will be incentivised to take action, with both sending and receiving firms splitting the costs of reimbursement 50:50. Publication of APP scams performance data: The inaugural report was released in October, showing for the first time how well banks and other payment firms performed in tackling APP scams and how they treated those who fell victim. Enhanced information sharing: Improved intelligence-sharing between PSPs so they can improve scam prevention in real time is expected to be implemented in early 2024. Because many of the scams start on social media or in fake advertisements, banks in the UK have made calls for the large tech firms (for example, Google, Facebook) and telcos to be included in the scam reimbursement process. As a first step to offer more protection for customers, in December 2022, the UK Parliament introduced a new Online Safety Bill that intends to make social media companies more responsible for their users’ safety by removing illegal content from their platforms. In November 2023, a world-first agreement to tackle online fraud was reached between the UK government and some of the leading tech companies - Amazon, eBay, Facebook, Google, Instagram, LinkedIn, Match Group, Microsoft, Snapchat, TikTok, X (Twitter) and YouTube. The intended outcome is for people across the UK to be protected from online scams, fake adverts and romance fraud thanks to an increased security measures that include better verification procedures and removal of any fraudulent content from these platforms. Outside of the UK, approaches to protect customers from APP fraud and social engineering scams are present in a few other jurisdictions. In the Netherlands, banks reimburse victims of bank impersonation scams when these are reported to the police and the victim has not been ‘grossly negligent.’ In the US, some banks provide voluntary reimbursement in cases of bank impersonation scams. As of June 2023, payment app Zelle, owned by seven US banks, has started refunding victims of impersonation scams, thus addressing earlier calls for action related to reported scams on the platform. In the EU, with the newly proposed Payment Services Directive (PSD3), issuers will also be liable when a fraudster impersonates a bank’s employee to make the user authenticate the payment (subject to filling in a police report and the payer not acting with gross negligence). In October 2023, the Monetary Authority of Singapore (MAS) proposed a new Shared Responsibility Framework that assigns financial institutions and telcos relevant duties to mitigate phishing scams and calls for payouts to be made to affected scam victims where these duties are breached. While this new proposal only includes unauthorised payments, it is unique because it is the first such official proposal that includes telcos in the reimbursement process. Earlier this year, the National Anti-Scam Centre in Australia, announced the start of an investment scam fusion cell to combat investment scams. The fusion cell includes representatives from banks, telcos, and digital platforms in a coordinated effort to identify methods for disrupting investment scams to minimise scam losses. To add to that, in November 2023, Australian banks announced the introduction of confirmation-of-payee system that is expected to help reduce scams by ensuring customers can confirm they are transferring money to the person they intend to, similarly to what has been done in the UK a few years ago. Finally, over the past few months, more jurisdictions such as Australia, Brazil, the EU and Hong Kong, have announced either proposals or the roll out of fraud data sharing schemes between banks and financial institutions. While not all of these schemes are directly tied to social engineering scams, they could be seen as a first step to tackle scams together with other types of fraud. While many jurisdictions beyond the UK are still in the early stages of the legislative process to protect consumers from scams, there is an expectation that regulatory changes that prove to be successful in the UK could be adopted elsewhere. This should help introduce better tracking of the problem, to stimulate collaboration between financial insitutions, and add visibility of financial instituitions efforts to prevent these types of fraud. As more countries introduce new regulations and more financial institutions start monitoring their systems for scams occurrences, the industry should be able to achieve greater success in protecting consumers and mitigating APP fraud and social engineering scams. How financial institutions can prevent APP fraud Changing regulations have initiated the first liability shifts towards financial institutions when it comes to APP fraud, making fraud prevention measures a greater area of concern for many leaders in the industry. Now the responsibility is spreading across both the sending and receiving payment provider, they also need to improve monitoring for incoming payments. What’s more, as these types of fraud are a global phenomenon, financial institutions from multiple jurisdictions might consider taking greater fraud prevention steps early on (before regulators impose any mandatory rules) to keep their customers safe and their reputation high. Here are five ways businesses can keep customers safe, while retaining brand reputation: Advanced analytics – advanced data analytics capabilities to create a 360° of individuals and their behaviour across all connected current accounts. This supports more sophisticated and effective fraud risk analysis that goes beyond a single transaction. Combining it with a view of fraudulent behaviours beyond the payment institution's premises by adding the ability to ingest data from multiple sources and develop models at scale allows businesses to monitor new fraud patterns and evolving threats. Behavioural biometrics – used to provide insights on indicators such as active mobile phone calls, session length, segmented typing, hesitation, and displacement to detect if the sender is receiving instructions over the phone or if they show unusual behaviour during the time of the transaction. Transaction monitoring and anomaly detection – required to monitor sudden spikes in transaction activity that are unusual for the sender of the funds as well as mule account activity on the receiving bank’s end. Fraud data sharing capabilities – sharing of fraud data across multiple organisations can help identify and stop risky transactions early, in addition to mitigation of mule activity and fraudulent new accounts opening. Monitoring of newly opened accounts – used to detect fake accounts or newly opened mule accounts. By leveraging a combination of these capabilities, financial institutions will be better prepared to cope with new regulations and support their customers in APP fraud. Identity & Fraud Report 2023 US Identity & Fraud Report 2023 UK Defeating Fraud Report 2023 EMEA & APAC

What are lenders prioritising when it comes to Gen AI? We take a look at five transformative use cases in lending, and organisational priorities for integrating Gen AI into customer lifecycle processes. Although Generative Artificial Intelligence (Gen AI) only launched publicly in the form of Chat GPT last November, adoption has been widespread and rapid. Even in typically risk-adverse industries like financial services, our research shows that there is widespread recognition that Gen AI could deliver a range of benefits across business functions. We identified five areas of focus for lenders based on our research. In a study conducted by Forrester Consulting on behalf of Experian, we surveyed 660 and interviewed 60 decision makers for technology purchases that support the credit lifecycle at their financial services organisation. The study included businesses across North America, UK and Ireland, and Brazil. The qualitative research showed that lenders are already using a type of Gen AI, Large Language Models (LLMs), in their operations, with a focus on testing across areas such as customer service and internal processes before deploying to credit operations. We look at the potential use cases, and how businesses are using Gen AI now. 1. Personalised customer experience Customers today expect a personalised lending experience that is tailored to their unique needs and preferences. GenAI can leverage customer data to generate personalised loan offers, recommendations, and repayment plans. This helps lenders improve customer satisfaction and loyalty, leading to increased customer retention and revenue growth. This is an area that is front of mind for the companies in our research – nearly half of businesses surveyed are planning to implement or expand technology capabilities to either upsell or retain customers in the next 12 months. Furthermore, 50% of companies believe that offering more tailored underwriting and pricing is a top priority in their credit operations, followed by 44% who also aim to increase personalisation in marketing, products, and services to their customers. According to the research, some organisations have formed alliances with technology providers like OpenAI and Microsoft to investigate and further explore the use of LLMs. These partnerships involve analysing customer data to identify opportunities for cross-selling. 2. Enhancing models with new data sources With new data sources emerging all the time, Gen AI is one of the technologies that will most likely accelerate the opportunity for businesses to incorporate them into models. Lenders could include sources such as social network data into their models by using LLMs. This unstructured data, including customer emotions and behaviours on social networks, would be treated as an additional variable in the models. According to the research social media data and psychometric data is already used across financial services, to varying degrees. It showed that 35% of retail companies use social media data, while 29% of FinTechs use psychometric data. Auto finance companies sit at lower end of the adoption scale, with only 12% using social media data and 15% psychometric data. 3. Operational efficiencies Gen AI can help bring operational efficiencies to customerjourneys across the entire lifecycle, offering lenders theability to automate and streamline various processes,resulting in improved productivity, cost savings, andenhanced customer experiences. One of the top challenges for businesses surveyed isimproving customer journeys during onboarding, and thiswas particularly significant for credit unions / buildingsocieties (53%). 4. Detecting and preventing fraud Gen AI can play a crucial role in fraud detection by analysing patterns and anomalies in vast datasets. By leveraging machine learning techniques, Gen AI models can proactively identify potentially fraudulent activities and mitigate risks. The ability to detect fraud in real-time improves the overall security of lending operations and helps protect lenders and borrowers from financial losses. Detecting and preventing fraud is a constant challenge for lenders. 51% of retailers and 47% of credit unions/ building societies surveyed said that reducing fraud losses is a key challenge for them. 5. Customer service Driven by advances in the machine learning and AI space, the world of customer service has benefited hugely from the adoption of virtual assistants and chatbots in recent years. This looks to continue, with businesses saying that LLMs are being tested for customer service purposes, allowing lenders to identify customer issues and automate actions. What's next for lenders? The research found that lenders are utilising various machine learning techniques like regression, decision trees, neural networks, and random forest, along with LLMs. Businesses are in the early stages of exploring how they can use LLMs in credit risk models, but it will undoubtedly involve a blend of existing and new capabilities. As with any emerging technology, it’s important to look at potential risk. The research indicated that organisations see challenges and concerns when it comes to the use of LLMs in their models. It is crucial to ensure the models are trusted, validated, and properly understood to avoid reliance on outsourced solutions and maintain control and visibility over the models’ functions. The ability to explain decisions in Gen AI to avoid bias can be difficult, and businesses will be watching the regulators to understand how best to proceed. There is no doubt, however, that Gen AI will optimise the credit customer lifecycle, creating vast opportunities for lenders. Download PDF More on Gen AI

In a study conducted by Forrester Consulting on behalf of Experian, we surveyed 660 and interviewed 60 decision makers for technology purchases that support the credit lifecycle at their financial services organisation. The study included businesses across North America, UK and Ireland, and Brazil. More on Gen AI

Fraud prevention is a critical concern for businesses today. To help combat this ever-present threat, the consortium approach has emerged as a powerful tool in the fight against fraud. By pooling resources, expertise, and creating visibility, consortium members can be more effective in detecting and preventing fraudulent activities. con-sor-tium noun: A group of people, countries, companies, etc., who are working together on a particular project. What is a consortium? Within business, consortiums are a global concept and can operate under multiple categories, including finance, marketing, and tech. A well-known, successful example is Star Alliance. They are a group of airlines, whose agreement enables their members to share and benefit from flights, airport lounges, and frequent flyer programs. All Star Alliance members are working towards the same goal, which is to offer their customers a seamless travel experience. Key benefits of the consortium approach Resource sharing: Pooling resources like funding, expertise, and infrastructure can lead to cost savings and efficient resource utilisation. Risk mitigation: Shared risks make it easier for organisations to tackle ambitious projects or ventures with reduced individual exposure. Access to expertise: Members can tap into the collective knowledge and skills of the consortium, enhancing their capabilities. Market influence: Consortiums often have more influence in negotiations, regulations, and standards-setting, benefiting all members. Innovation: Collaboration can foster innovation through cross-pollination of ideas and technologies among members. Economies of scale: Consortiums can negotiate better deals on purchases or services due to their combined purchasing power. Reduced competition: In some cases, members can reduce direct competition among themselves by coordinating efforts. Market entry: Consortiums can facilitate market entry, especially in foreign markets, by leveraging each other's networks and knowledge. Shared infrastructure: Access to shared facilities or infrastructure can save costs and accelerate projects. Brand recognition: Being part of a reputable consortium can enhance an organisation's credibility and market presence. However, consortiums also come with challenges such as coordination issues, conflicts of interest, and shared decision-making. Successful consortiums require effective governance structures and clear agreements among members. Consortiums in fraud detection and prevention The success of a consortium relies on the collective commitment of its members to a shared goal. In the context of fraud prevention, this means maintaining consistent and high-quality insights across all members. To achieve this, consortium members adhere to an agreement that covers elements such as data quality and data frequency. These agreements ensure that all participants contribute their best insights and information. By fostering a culture of cooperation and sharing, consortiums create an environment where valuable insights can be harnessed to combat fraud effectively. However, it's crucial to emphasise that the success of consortiums ultimately depends on the active participation and contribution of all its members. Consortiums can only thrive when every member is dedicated to making their quality insights accessible to the group. Read more about how consortiums can revolutionise fraud detection and prevention by sharing data on fraudsters across different product types and industry sectors with Hunter.

With an ever-growing number of data sources, businesses must be able to rapidly access and integrate them into decisioning processes using no-code tools to stay ahead of the competition. Today’s customer journey has become increasingly sophisticated. As most firms that interact with customers can attest, this journey is a dynamic process shaped by a range of decisions. Businesses need to decide what is the most compelling offer to deliver to a new customer. Should you approve their loan application? Could the customer gain more from sustainability-linked loans or greener mortgages? What is rich data? These diverse decisions are ideally informed by rich data. This is all the available data, including new data derived from analytics using advanced techniques such as Machine Learning and using rules to make predictions and to calculate scores. While most firms have this data, it is difficult to gather, prepare and integrate into the decisioning processes. Multiplicity of data sources Data types and sources are growing. With regulatory bodies gradually approving the use of more data globally, businesses are faced with an opportunity dressed up as a challenge. Speedy integration of different data sources gives organizations a competitive edge, so finding vendors that can enable firms to utilize available data will positively impact them from a cost efficiency perspective, while also creating the potential for revenue growth. The future is to empower business users with no-code data management No-code data management capabilities add a whole new meaning to self-sufficiency for businesses. It will enable teams across organizations to rapidly change data-driven strategies without much vendor involvement. Gartner estimates that by 2025, 70% of new applications developed by enterprises will use low-code or no-code technologies, up from less than 25% in 2020.   Moving towards client self-service with no-code capabilties is the goal of most businesses. These capabilities are already allowing teams supporting clients to rapidly integrate data sources into their solutions, providing the perfect test ground for business user enablement. If a decision strategy requires changes and a new data source, PowerCurve users can quickly adapt. They can now gather and prepare the right data and deliver it to the system within days. These changes can be instantly published through secure and easily adjustable APIs that support the latest industry standards and frameworks such as OpenAPI and OAuth. An effective customer journey relies on informed decisions and these decisions rely on the right data and advanced analytics. While Experian's PowerCurve platform is well known for automating a range of decisions across the customer lifecycle, it is the data integration capabilities that ensure these decisions are informed by rich data and insights. Creating a harmonious relationship that produces superior and trustworthy results for businesses. No-code data management enables businesses with easy and rapid data source access to deliver rich and insightful data to decisioning processes.

With heightened consumer demand for an improved customer experience online, and the increasing threat of fraud, how can organizations ensure secure and efficient customer onboarding in today's digital landscape? Onboarding the highest number of customers while maintaining compliance and security Digital account opening is in demand. Businesses are competing to create the most effective onboarding experience, while managing the need to draw on multiple sources during account opening. The onboarding stage of the customer lifecycle plays a pivotal role in establishing trust between the customer and the business. Friction during the digital account opening process can lead to customer dropouts, resulting in lower growth for organizations. Moreover, the ever-present threat of fraud necessitates organizations to be vigilant and enhance customer journey with an added layer of verification and protection. Liminal, a leading market intelligence firm specializing in digital identity, cybersecurity, and fintech markets, recently recognized Experian as a market leader for compliance and fraud prevention capabilities and execution in its Liminal Link Index on Account Opening in Financial Services. Download report The report highlights that solution providers in financial services are focused on delivering high levels of assurance while maintaining regulatory compliance and minimizing user friction. Access to real-time verification data, risk analytics and decision-making strategies make it possible for clients to verify identities, detect and prevent fraud, and ensure regulatory compliance. Experian’s identity verification and fraud prevention solutions, including CrossCore® and Precise ID®, received the highest Link Score out of the 32 companies highlighted in the report. It found that Experian was recognized by 94% of buyers and 89% identified Experian as a market leader. “We’re thrilled to be named the top market leader in compliance and fraud prevention capabilities and execution by Liminal’s Link Index Report. We’re continually innovating to deliver the most effective identity verification and fraud prevention solutions to our clients so they can grow their business, mitigate risk and provide a seamless customer experience.”Kathleen Peters, Chief Innovation Officer for Experian’s Decision Analytics business in North America The report offers valuable insights into the market overview, demands, challenges, purchasing criteria, vendor landscape, landscape analysis, and buyer opportunities. Access full report

In today's fast-paced digital landscape, businesses are inundated with an unprecedented amount of data and information. Making informed decisions with the data quickly and effectively has become a crucial factor for success. Enter digital decisioning—a transformative approach that harnesses the power of data, analytics, and automation to drive reliable and expedited decision-making. This article delves into the world of digital decisioning, exploring its significance, components, and benefits.  The Essence of Digital Decisioning  At its core, digital decisioning is the process of leveraging software solutions that use digital decisioning platforms or custom-built engines to author decision logic; use decision intelligence technologies such as machine learning and AI; use digital decisions in vertical and horizontal use cases; and manage the full decision logic lifecycle, including feedback loops, to continuously improve decision logic. It enables organizations to make well-informed choices by automating and optimizing complex decision processes. By amalgamating data from various sources in real-time, including credit data, user behavior, market trends, historical data, and external factors, digital decisioning ensures that timely decisions are not only data-driven but also contextually relevant.  Components of Digital Decisioning  Continuous Data Feed: This is the lifeblood of digital decisions. Organizations normalize data from disparate sources to form comprehensive and accurate datasets. Customer data might include income, credit history, transactional data, bill payment, or digital footprint data; however, regardless of the sources, it’s critical that data is coalesced into a single, virtualized view.   Advanced Analytics and Machine Learning: Analytics and machine learning algorithms are deployed to extract meaningful insights from the collected data. These insights are used to model decision scenarios, predict outcomes, and uncover hidden patterns.  Decision Models: Decision models are created based on the insights derived from data analysis. These models define the rules and logic for making decisions, incorporating factors such as risk tolerance, business goals, and regulatory compliance.   Direct Feedback Loop: Every decision has an outcome. For example, an automated loan offer is either accepted or declined by the customer. These outcomes — good and bad — automatically feed into the decisioning model, which enables the machine learning technology to “learn” which decisions are optimal, given the circumstances and customer profile. This enables the model to adapt and grow more accurately and precisely over time.  Automation: Automation engines execute the decision models in real time, allowing for rapid and consistent decision-making without human intervention. This enhances efficiency and minimizes the risk of errors.  According to a 2022 Gartner poll, the CIO Agenda, more than 80% of companies plan to keep or grow their investment in automation solutions.  Benefits of Digital Decisioning  Enhanced Accuracy: Digital decisioning eliminates human biases and inconsistencies, resulting in more accurate and objective decisions.  Improved Efficiency: Automation reduces decision-making time from hours or days to milliseconds, enabling organizations to respond swiftly to market changes and customer demands.  Hyper Personalization: By considering individual preferences, behaviors, and history, digital decisioning facilitates the creation of tailored experiences for customers, leading to higher satisfaction and engagement.  Scalability: The automated nature of digital decisioning ensures that it can handle a high volume of decisions seamlessly, making it ideal for businesses experiencing rapid growth.  Regulatory Compliance: Explainable decision models can be designed to incorporate regulatory guidelines and compliance requirements, reducing the risk of legal complications. Use Case: Respond faster to credit card applications and personalize cross-sell offers  Customers apply online for a credit card from a bank. As they’re being pre-qualified, digital decisioning will instantly analyze the customers’ accounts with the bank including disclosed and undisclosed cash flow. A digital decisioning software solution enables the bank to assess risk exposure and anticipate the customer’s immediate need(s), thereby automating the application assessment and approval steps to reduce approval times from weeks to minutes. Based on the bank’s comprehensive understanding of that customer at that moment, it triggers a personalized cross-sell offer for another relevant financial product, automatically boosting incremental revenue.  Conclusion Digital decisioning marks a pivotal advancement in how choices are made in business. By harnessing the power of data, analytics, and automation, organizations can make faster, more accurate decisions that are aligned with their goals and market realities. As this technology continues to evolve, it will reshape industries and empower individuals to navigate the complex digital landscape with confidence.   Experian’s decisioning management platform allows clients to operationalize the power of rich data, advanced analytics, and automated decisioning software to support the customer lifecycle. Its key differentiators include credit risk, fraud risk, and strategy expertise, fast deployment of strategies into test and production, empowerment of business users, and proactive monitoring of strategy performance by users. Its key use cases include reducing acquisition costs, credit risk, and fraud risk, and improving acceptance rate and the customer journey.  Experian has been named a Technology Leader in the August 2023 SPARK Matrix on Digital Decisioning Platforms report published by Quadrant Knowledge Solutions.  The report highlights the growth of decisioning platforms and the changing market trends that are driving adoption, including the role machine learning and AI are playing in the technology market. This placement is proof that Experian offers best-in-class capabilities through market-leading data, orchestration and automation, advanced analytical models, decision performance, and reporting. Our cloud-based infrastructure enables a scalable and modular platform that allows our solutions to be suitable for customers of all sizes.   Read the report Experian’s Decisioning Management Platform: Accelerating analytics, decisioning, and fraud detection automation Continuous improvement loop: Advanced machine learning models improve decisioning quality