Financial Solutions

Third-party fraud involves an identifiable victim that is willing to collaborate in the investigation and resolution.

Account takeover fraud can be costly, but is preventable with the right account takeover fraud prevention solution.

For companies that regularly engage in financial transactions, having a customer identification program (CIP) is mandatory to comply with the regulations around identity verification requirements across the customer lifecycle. In this blog post, we will delve into the essentials of a customer identification program, what it entails, and why it is important for businesses to implement one. What is a Customer Identification Program (CIP)? A CIP is a set of procedures implemented by financial institutions to verify the identity of their customers. The purpose of a CIP is to be a part of a financial institution’s fraud management solutions, with similar goals as to detect and prevent fraud like money laundering, identity theft, and other fraudulent activities. The program enables financial institutions to assess the risk level associated with a particular customer and determine whether their business dealings are legitimate. An effective CIP program should check the following boxes: Confidently verify customer identities Seamless authentication Understand and anticipate customer activities Where does Know Your Customer (KYC) fit in? KYC policies must include a robust CIP across the customer lifecycle from initial onboarding through portfolio management. KYC solutions encompass the financial institution’s customer identification program, customer due diligence and ongoing monitoring. What are the requirements for a CIP? Customer identification program requirements vary depending on the type of financial institution, the type of account opened, and other factors. However, the essential components of a CIP include verifying the customer's identity using government-issued identification, obtaining and verifying the customer's address, and checking the customer against a list of known criminals, terrorists, or suspicious individuals. These measures  help detect and prevent financial crimes. Why is a CIP important for businesses? CIP helps businesses mitigate risk by ensuring they have accurate and up-to-date information about their customers. This also helps financial institutions comply with laws and regulations that require them to monitor financial transactions for any suspicious activities. By having a robust CIP in place, businesses can establish trust and rapport with their customers. According to Experian’s 2023 U.S. Identity and Fraud Report, more than 85% of consumers expect businesses to respond to their identity and fraud concerns, and these expectations have risen over the past several years. Having an effective CIP in place is part of financial institutions showing their consumers that they have their best interests top of mind. Finding the right partner It’s important to find a partner you trust when working to establish processes and procedures for verifying customer identity, address, and other relevant information. Companies can also utilize specialized software that can help streamline the CIP process and ensure that it is being carried out accurately and consistently. Experian’s proprietary and partner data sources and flexible monitoring and segmentation tools allow you to resolve CIP discrepancies and fraud risk in a single step, all while keeping pace with emerging fraud threats with effective customer identification software. Putting consumers first is paramount. The security of their identity is priority one, but financial institutions must pay equal attention to their consumers’ preferences and experiences. It is not just enough to verify customer identities. Leading financial institutions will automate customer identification to reduce manual intervention and verify with a reasonable belief that the identity is valid and eligible to use the services you provide. Seamless experiences with the right amount of friction (I.e., step-up authentication) should also be pursued to preserve the quality of the customer experience. Putting it all together As cybersecurity threats are becoming more sophisticated, it is essential for financial institutions to protect their customerinformation and level up their fraud prevention solutions. Implementing a customer identification program is an essential component in achieving that objective. A robust CIP helps organizations detect, prevent, and deter fraudulent activities while ensuring compliance with regulatory requirements. While implementing a CIP can be complex, having a solid plan and establishing clear guidelines is the best way for companies to safeguard customer information and maintain their reputation. CIPs are an integral part of financial institutions security infrastructures and must be a business priority. By ensuring that they have accurate and up-to-date data on their customers, they can mitigate risk, establish trust, and comply with regulatory requirements. A sound CIP program can help financial institutions detect and prevent financial crimes and cyber threats while ensuring that legitimate business transactions are not disrupted, therefore safeguarding their customers' information and protecting their own reputation. Learn more

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First-party fraud can be detected and prevented by using robust fraud risk management strategies and solutions.

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Model governance is growing increasingly important as more companies implement machine learning model deployment and AI analytics solutions into their decision-making processes. Models are used by institutions to influence business decisions and identify risks based on data analysis and forecasting. While models do increase business efficiency, they also bring their own set of unique risks. Robust model governance can help mitigate these concerns, while still maintaining efficiency and a competitive edge. What is model governance? Model governance refers to the framework your organization has in place for overseeing how you manage your development, model deployment, validation and usage.1 This can involve policies like who has access to your models, how they are tested, how new versions are rolled out or how they are monitored for accuracy and bias.2 Because models analyze data and hypotheses to make predictions, there's inherent uncertainty in their forecasts.3 This uncertainty can sometimes make them vulnerable to errors, which makes robust governance so important. Machine learning model governance in banks, for example, might include internal controls, audits, a thorough inventory of models, proper documentation, oversight and ensuring transparent policies and procedures. One significant part of model governance is ensuring your business complies with federal regulations. The Federal Reserve Board and the Office of the Comptroller of the Currency (OCC) have published guidance protocols for how models are developed, implemented and used. Financial institutions that utilize models must ensure their internal policies are consistent with these regulations. The OCC requirements for financial institutions include: Model validations at least once a year Critical review by an independent party Proper model documentation Risk assessment of models' conceptual soundness, intended performance and comparisons to actual outcomes Vigorous validation procedures that mitigate risk Why is model governance important — especially now? More and more organizations are implementing AI, machine learning and analytics into their models. This means that in order to keep up with the competition's efficiency and accuracy, your business may need complex models as well. But as these models become more sophisticated, so does the need for robust governance.3 Undetected model errors can lead to financial loss, reputation damage and a host of other serious issues. These errors can be introduced at any point from design to implementation or even after deployment via inappropriate usage of the model, drift or other issues. With model governance, your organization can understand the intricacies of all the variables that can affect your models' results, controlling production closely with even greater efficiency and accuracy. Some common issues that model governance monitors for include:2 Testing for drift to ensure that accuracy is maintained over time. Ensuring models maintain accuracy if deployed in new locations or new demographics. Providing systems to continuously audit models for speed and accuracy. Identifying biases that may unintentionally creep into the model as it analyzes and learns from data. Ensuring transparency that meets federal regulations, rather than operating within a black box. Good model governance includes documentation that explains data sources and how decisions are reached. Model governance use cases Below are just three examples of use cases for model governance that can aid in advanced analytics solutions. Credit scoring A credit risk score can be used to help banks determine the risks of loans (and whether certain loans are approved at all). Governance can catch biases early, such as unintentionally only accepting lower credit scores from certain demographics. Audits can also catch biases for the bank that might result in a qualified applicant not getting a loan they should. Interest rate risk Governance can catch if a model is making interest rate errors, such as determining that a high-risk account is actually low-risk or vice versa. Sometimes changing market conditions, like a pandemic or recession, can unintentionally introduce errors into interest rate data analysis that governance will catch. Security challenges One department in a company might be utilizing a model specifically for their demographic to increase revenue, but if another department used the same model, they might be violating regulatory compliance.4 Governance can monitor model security and usage, ensuring compliance is maintained. Why Experian? Experian® provides risk mitigation tools and objective and comprehensive model risk management expertise that can help your company implement custom models, achieve robust governance and comply with any relevant federal regulations. In addition, Experian can provide customized modeling services that provide unique analytical insights to ensure your models are tailored to your specific needs. Experian's model risk governance services utilize business consultants with tenured experience who can provide expert independent, third-party reviews of your model risk management practices. Key services include: Back-testing and benchmarking: Experian validates performance and accuracy, including utilizing statistical metrics that compare your model's performance to previous years and industry benchmarks. Sensitivity analysis: While all models have some degree of uncertainty, Experian helps ensure your models still fall within the expected ranges of stability. Stress testing: Experian's experts will perform a series of characteristic-level stress tests to determine sensitivity to small changes and extreme changes. Gap analysis and action plan: Experts will provide a comprehensive gap analysis report with best-practice recommendations, including identifying discrepancies with regulatory requirements. Traditionally, model governance can be time-consuming and challenging, with numerous internal hurdles to overcome. Utilizing Experian's business intelligence and analytics solutions, alongside its model risk management expertise, allows clients to seamlessly pass requirements and experience accelerated implementation and deployment. Experian can optimize your model governance Experian is committed to helping you optimize your model governance and risk management. Learn more here. References 1Model Governance," Open Risk Manual, accessed September 29, 2023. https://www.openriskmanual.org/wiki/Model_Governance2Lorica, Ben, Doddi, Harish, and Talby, David. "What Are Model Governance and Model Operations?" O'Reilly, June 19, 2019. https://www.oreilly.com/radar/what-are-model-governance-and-model-operations/3"Comptroller's Handbook: Model Risk Management," Office of the Comptroller of the Currency. August 2021. https://www.occ.treas.gov/publications-and-resources/publications/comptrollers-handbook/files/model-risk-management/pub-ch-model-risk.pdf4Doddi, Harish. "What is AI Model Governance?" Forbes. August 2, 2021. https://www.forbes.com/sites/forbestechcouncil/2021/08/02/what-is-ai-model-governance/?sh=5f85335f15cd

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Data-driven machine learning model development is a critical strategy for financial institutions to stay ahead of their competition, and according to IDC, remains a strategic priority for technology buyers.  Improved operational efficiency, increased innovation, enhanced customer experiences and employee productivity are among the primary business objectives for organizations that choose to invest in artificial intelligence (AI) and machine learning (ML), according to IDC’s 2022 CEO survey.   While models have been around for some time, the volume of models and scale at which they are utilized has proliferated in recent years. Models are also now appearing in more regulated aspects of the business, which demand increased scrutiny and transparency.   Implementing an effective model development process is key to achieving business goals and complying with regulatory requirements. While ModelOps, the governance and life cycle management of a wide range of operationalized AI models, is becoming more popular, most organizations are still at relatively low levels of maturity. It's important for key stakeholders to implement best practices and accelerate the model development and deployment lifecycle.   Read the IDC Spotlight Challenges impeding machine learning model development  Model development involves many processes, from wrangling data, analysis, to building a model that is ready for deployment, that all need to be executed in a timely manner to ensure proper outcomes. However, it is challenging to manage all these processes in today’s complex environment.   Modeling challenges include:  Infrastructure: Necessary factors like storage and compute resources incur significant costs, which can keep organizations from evolving their machine learning capabilities.   Organizational: Implementing machine learning applications requires talent, like data scientists and data and machine learning engineers.  Operational: Piece meal approaches to ML tools and technologies can be cumbersome, especially on top of data being housed in different places across an organization, which can make pulling everything together challenging.  Opportunities for improvement are many While there are many places where individuals can focus on improving model development and deployment, there are a few key places where we see individuals experiencing some of the most time-consuming hang-ups.   Data wrangling and preparation   Respondents to IDC's 2022 AI StrategiesView Survey indicated that they spend nearly 22% of their time collecting and preparing data. Pinpointing the right data for the right purpose can be a big challenge. It is important for organizations to understand the entire data universe and effectively link external data sources with their own primary first party data. This way, stakeholders can have enough data that they trust to effectively train and build models.   Model building  While many tools have been developed in recent years to accelerate the actual building of models, the volume of models that often need to be built can be difficult given the many conflicting priorities for data teams within given institutions. Where possible, it is important for organizations to use templates or sophisticated platforms to ease the time to build a model and be able to repurpose elements that may already be working for other models within the business.   Improving Model Velocity Experian’s Ascend ML BuilderTM is an on-demand advanced model development environment optimized to support a specific project. Features include a dedicated environment, innovative compute optimization, pre-built code called ‘Accelerators’ that simply, guide, and speed data wrangling, common analyses and advanced modeling methods with the ability to add integrated deployment.  To learn more about Experian’s Ascend ML Builder, click here.   To read the full Technology Spotlight, download “Accelerating Model Velocity with a Flexible Machine Learning Model Development Environment for Financial Institutions” here.  Download spotlight *This article includes content created by an AI language model and is intended to provide general information. 

Fraudsters have evolved their techniques to capitalize on homeowners and lenders by shifting their focus from home purchases to HELOC fraud.

In today's fast-paced financial landscape, financial institutions must stay ahead of the curve when it comes to account opening and onboarding. Digital account opening, empowering a prospective client to securely and efficiently open a new account, is key to how banks, credit unions and other financial institutions grow their business and expand their portfolio. Regardless of the time, money and other resources a financial institution invests in marketing to the right target prospect and tailoring an attractive offer, it’s worthless if that prospective customer can’t complete the process due to a poor account opening experience. Unhappy customers vote with their feet. A recent Experian study found that of the more 2,000 consumers surveyed who’d opened a new account in the last six months, 37% took their business elsewhere due to a negative account opening experience.   The choice of a reliable partner can make all the difference to your account opening and onboarding experience. The right partner must provide your financial institution with access to the freshest credit data; advanced analytics, scores and models to empower you to say yes to the right customers that meet your lending criteria; and industry-leading decision engines that make the best decisions and enable you to provide a seamless customer experience.  Moreover, the right partner will also help you in maintaining high levels of security without compromising user experience, all while adhering to regulatory compliance.  Recently, Liminal, a leading advisory and market intelligence firm specializing in the digital identity, cybersecurity, and fintech markets, released its highly anticipated Link™ Index Report for Account Opening in Financial Services, which evaluates solution providers in the financial sector, in the areas of compliance and fraud prevention for account opening. The report recognized Experian as a market leader for compliance and fraud prevention capabilities and market execution. Experian’s identity verification and fraud prevention solutions, including CrossCore® and Precise ID®, received the highest 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,” said Kathleen Peters, Chief Innovation Officer for Experian’s Decision Analytics business in North America. “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.”  You can access the full report here. To learn more about Experian’s award-winning fraud solutions, visit our identity fraud hub.  Download Liminal Link Index Report

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