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The fact that the last recession started right as smartphones were introduced to the world gives some perspective into how technology has changed over the past decade. Organizations need to leverage the same technological advancements, such as artificial intelligence and machine learning, to improve their collections strategies. These advanced analytics platforms and technologies can be used to gauge customer preferences, as well as automate the collections process. When faced with higher volumes of delinquent loans, some organizations rapidly hire inexperienced staff. With new analytical advancements, organizations can reduce overhead and maintain compliance through the collections process. Additionally, advanced analytics and technology can help manage customers throughout the customer life cycle. Let’s explore further: Why use advanced analytics in collections? Collections strategies demand diverse approaches, which is where analytics-based strategies and collections models come into play. As each customer and situation differs, machine learning techniques and constraint-based optimization can open doors for your organization. By rethinking collections outreach beyond static classifications (such as the stage of account delinquency) and instead prioritizing accounts most likely to respond to each collections treatment, you can create an improved collections experience. How does collections analytics empower your customers? Customer engagement, carefully considered, perhaps comprises the most critical aspect of a collections program—especially given historical perceptions of the collections process. Experian recently analyzed the impact of traditional collections methods and found that three percent of card portfolios closed their accounts after paying their balances in full. And 75 percent of those closures occurred shortly after the account became current. Under traditional methods, a bank may collect outstanding debt but will probably miss out on long-term customer loyalty and future revenue opportunities. Only effective technology, modeling and analytics can move us from a linear collections approach towards a more customer-focused treatment while controlling costs and meeting other business objectives. Advanced analytics and machine learning represent the most important advances in collections. Furthermore, powerful digital innovations such as better criteria for customer segmentation and more effective contact strategies can transform collections operations, while improving performance and raising customer service standards at a lower cost. Empowering consumers in a digital, safe and consumer-centric environment affects the complete collections agenda—beginning with prevention and management of bad debt and extending through internal and external account resolution. When should I get started? It’s never too early to assess and modernize technology within collections—as well as customer engagement strategies—to produce an efficient, innovative game plan. Smarter decisions lead to higher recovery rates, automation and self-service tools reduce costs and a more comprehensive customer view enhances relationships. An investment today can minimize the negative impacts of the delinquency challenges posed by a potential recession. Collections transformation has already begun, with organizations assembling data and developing algorithms to improve their existing collections processes. In advance of the next recession, two options present themselves: to scramble in a reactive manner or approach collections proactively. Which do you choose? Get started

There are thousands of potential car buyers heading into dealerships and browsing websites for their next vehicle every day. And that means thousands of opportunities for automotive manufacturers to market their vehicles to prospective buyers. But not every vehicle is going to meet the needs and wants of every car buyer. So, how do automotive brand marketers reach the individuals most likely to be interested in their products? Simply put, it comes down to better understanding the brand’s audience. But, today’s digitally-driven world creates a significant challenge for brand marketers – the overreliance on mobile devices and digital channels creates hundreds of digital touchpoints for brand marketers to consider. But, the data also creates an opportunity. If automotive marketers can bridge the gap between online and offline touchpoints, they’ll be better positioned to develop messages that resonate with their desired audience and deliver communications through the most effective channels. The end result? More meaningful interaction with potential car buyers. To get there, automotive marketers need to consider these concepts: Navigate the identity resolution process The secret to a more relevant conversation begins and ends with knowing who you are addressing. People interact with brands through a variety of channels. For example, a person may see an advertisement for a new vehicle on their smartphone, later research the same vehicle at home on their desktop or on a mobile app and test drive the vehicle a few days later. The automotive marketer that can reconcile these three different interactions will be able to deliver relevant advertisements to the individual and cut down on wasted advertising spend. Knowledge-based identity resolution is what allows you to be smarter with your marketing. Present the right offer People are bombarded with hundreds, if not thousands, of advertisements daily. Automotive marketers need to cut through the noise and deliver messages that resonate with the target audience – whether it’s through e-newsletters, 30-second TV spots, banner ads or direct mail pieces. If automotive manufacturers miss the mark, it could lead to a frustrated consumer and poor brand reputation. For instance, an automotive marketer would not want to advertise the latest minivan to a couple who are empty nesters. Create customer loyalty It’s important to stay on top of current market statistics and data to fine-tune marketing campaigns. Vehicle ownership and purchase patterns can vary greatly in each market, and that means brands might need to fine-tune long-term loyalty strategies. A loyalty program that works in the Northeast might not work well for the Midwest market based on car buying patterns and the reasons behind owning a car. Data can help prioritize resources in areas with the highest potential for sales growth. Experian Marketing EngineTM helps automotive manufacturers engage customers across every channel while making the most of a marketing budget. It’s designed to seamlessly collect, consolidate and use customer data by connecting offline and online identifiers to create a single customer view. Experian’s North American Vehicle Database alone has over 11 billion records and over 900 million vehicles, of which over 68 million are Canadian vehicles. Marketing Engine leverages automotive specific insights, including vehicle purchase behaviors and ownership data, and combines that with other marketing data such as demographics and lifestyle interests. These automotive tools provide a more unified approach so that brands can make more informed decisions, gain and retain new customers, and drive sales. Learn more at https://www.experian.com/automotive/marketing.

Today, Experian and Oliver Wyman announced the launch of Ascend CECL ForecasterTM, a solution built to help financial institutions of all sizes more quickly and accurately forecast lifetime credit losses. The Financial Accounting Standards Board’s current expected credit loss (CECL) model has been a hot discussion topic throughout the financial services industry – first when it was announced (and considered one of the most significant accounting changes in decades), and most recently with the FASB’s delay for implementation for smaller lenders. As the compliance deadlines approach, Experian and Oliver Wyman have joined forces to help financial institutions adhere their loan portfolios to the new guidelines. Delivered through Experian’s Ascend Technology PlatformTM, Ascend CECL Forecaster is a new user-friendly, web-based application that combines Experian’s vast loan-level data and Premier AttributesSM, third-party macroeconomic data, valuation data and Oliver Wyman’s industry-leading CECL modeling methodology to accurately calculate potential losses over the life of a loan. “Ascend CECL Forecaster is a critical capability needed urgently by all lending and financial institutions,” said Ash Gupta, a Senior Advisor to Oliver Wyman and former Chief Risk Officer for American Express, in a press release. “The collaboration between Experian and Oliver Wyman allows a frictionless synthesis of industry data, capabilities and experience to serve customers in both first and second line of defense.” The premise behind the model, which will need access to more data than that used to calculate reserves under the incurred loss model, Allowance for Loan and Lease Losses (ALLL), is for financial institutions to estimate the expected loss over the life of a loan by using historical information, current conditions and reasonable forecasts. Built using advanced machine learning and statistical techniques, the web-based application maximizes the more than 15 years of historical credit data spanning previous economic cycles to help financial institutions gauge loan portfolio performance under various scenarios. Ascend CECL Forecaster does not require additional data nor does it require a secondary integration from the financial institution and enables organizations to more quickly test their portfolios under different economic factors. Moreover, financial institutions receive guidance from industry experts to assist with implementation and strategy. Additionally, Experian and Oliver Wyman will host a webinar to help financial institutions better understand and prepare for the upcoming CECL standards. Register today! Read the Press Release Register for Webinar


