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  • @fintechna 3:35 am on July 25, 2018 Permalink | Reply
    Tags: , , 808080, , , , , imperative   

    The data imperative for credit cards 

    Over the past dozen years, numerous US regional have relaunched consumer card programs on a self-issued basis. At the outset, the growth component for many of these relaunch strategies relied heavily on branch channels, customer loyalty and the desire to consolidate banking relationships. In recent years, the banks’ credit card programs have been plateauing to low, single-digit growth rates without obvious incremental prospects for growth in accounts, spend and balances. Although credit card portfolio health and returns continue to be favorable, without the ability to demonstrate further stepwise growth potential, these programs are at risk of atrophy in key areas, such as attention from senior executives and ongoing investment in innovation.

    Often, the keys to reinvigorating growth include identifying and addressing root-cause growth inhibitors (which often relate to approval rates, credit line assignment and service experiences), and finding ways to digitize and integrate customers’ credit card experiences with those of their overall banking relationships. exhaust created by these card programs and other players in the payment value chain could hold a secret to vast amounts of information value to unlock growth opportunities.

    Card issuers and, in particular, the payments industry generally have been early adopters of data-driven insights to grow their business; and rightly so, since the industry generates a massive volume of data. Banks are increasingly recognizing and reaching the point at which they need to drive innovative applications of the insights in functions that traditionally do not leverage them fully or consistently—for example, for enhancing customer experience or devising new product strategies.

    In addition, as depicted in Figure 1, prospect and customer segmentation can be a key component of focusing growth strategy investments on areas of greatest opportunity. For instance, segmentation can help a bank determine areas for product refinement to both improve experiences for existing credit cardholders and tap into unserved or underserved markets. We also see segmentation as the prudent way for many banks to carefully venture outside of their existing retail banking customer bases through twinning analysis to identify characteristics their most profitable cardholder segments may share with non-relationship prospect pools.

    Figure 1. Actionable segmentation driving key customer/prospect insights
    The data imperative for credit cards fintech
    Source: Accenture research and analysis

    Card issuers see only one dimension of customers. However, there is significant information asymmetry with other players in the value chain, namely, payment networks, merchant acquirers and merchants. Issuers capture data about and cardholder details only, while merchant acquirers see details on merchants and transactions, merchants collect data on their customers and purchase basket, and the payment networks record data on movement of funds between these players and authorization tokens. Building a cross-payment cycle data view allows creation of rich micro-segments for hyper-personalization (Figure 2). It also enables banks to conduct merchant, store and product-level marketing studies, generate early warning indicators for fraud and delinquency, and create visibility into customer and industry trends.

    Figure 2. Types of data captured and analyzed across different parties involved during the payments process
    The data imperative for credit cards fintech
    Source: Accenture research and analysis

    Collection, cleaning and deciphering this data exhaust is an onerous task. However, advancements in artificial intelligence capabilities, like machine learning and Big Data, is making it easier and faster than ever before.

    Capabilities, such as Accenture’s Intelligent Enterprise Platform that sits on top of the Accenture Insights Platform allows banks to layer third-party data from social media, web browsing and geo-tagging over the payments data. This further deepens card issuers’ understanding by manifolds around customer needs and behavior. It’s opening previously unimagined use cases, like real-time mood-/persona-based recommendations, geo-tagging and location-based offers to customers.

    Looking forward, we anticipate that a cross-payment cycle data ecosystem together with machine learning will play a broader role in how banks generate new growth in accounts, spend and balances, as well as how they harvest value in their credit card programs.

    We invite you to read about data as the new ecosystem currency in our report, The New, New Normal: Exponential Growth

    Special thanks to Sanjay Ojha for his insights, as well as Rajat Mawkin and Uday Gupta, who also contributed to this blog. 

    The post The data imperative for credit cards appeared first on Accenture Banking Blog.

    Accenture Banking Blog

     
  • @fintechna 3:35 am on May 14, 2018 Permalink | Reply
    Tags: , 808080, , , coaster, , , , riskreturns, roller   

    The risk-returns roller coaster for US consumer credit cards 

    Since the global financial crisis, have become a relatively stable and profitable asset class within US retail banking. However, with increasing movement across multiple, high-visibility areas of the credit card P&L from rates to rewards and charge-offs, issuers and their stakeholders are asking, “How are we performing?” and in a larger sense, “How should we be evaluating program performance?” An illustrative scan of publicly disclosed key performance indicators—such as interest yields on credit card loans, credit line utilization rates, and return on equity—provides topical insights into the complexities of a credit card portfolio, the risk of “mono-variabilitis” at portfolio levels, and the importance of evaluating performance holistically within the context of the customer portfolio, business strategy, and operational capabilities of different issuers.

    Interest Yields

    At a cardholder level, most large and mid-sized credit card issuers assess higher annual percentage rates (APRs) for cardholders deemed to be more at risk for payment default, a concept generally known as risk-based pricing. As would be anticipated, empirical data from US FDIC call reports for the top 100 US financial institutions (FIs) with at least $ 10 million in credit card loans (Figure 1) depicts a correlation between (a) interest yield, which is the weighted average APRs on revolving balances divided by revolving and transacting balances, and (b) net charge-off rates on credit card loans.

    What is interesting is the substantial statistical variance at the portfolio level that cannot be explained by just looking at rates and charge-offs, even when segmented by portfolio type. From our past experience with credit card portfolios, sources of this variance are wide-ranging and interlinked: from customer heterogeneity and different product types (the upper right of the dot plot of Figure 1, for example, that includes several card portfolios focused on the “building credit” consumer segment, such as secured cards) to variance in customer treatment and other portfolio management practices throughout the account lifecycle.

    Reflecting the wider range of factors, just because an issuer is over- (under-) indexing the line, with higher (lower) yield at a particular charge-off level, does not necessarily mean the business is over- (under-) performing. Even for common and widely held relationships at the cardholder level, the portfolio picture is more complex and calls for knowledge of both the pieces and the interlinked relationships to ascertain the business meaning of relative industry performance.

    Figure 1:  Interest Yield vs. Net Charge-Offs on Credit Cards

    The risk-returns roller coaster for US consumer credit cards fintech

    Source: Accenture analysis of FDIC call report data for US commercial , savings banks, and savings & loan associations with at least $ 10 million in consumer credit card balances as of year-end 2017. National Banks had $ 10+ billion in credit card receivables for the period; Super Regional Banks had $ 1 to $ 9.9 billion; Regional Banks had $ 100 to $ 999.9 million; and, Community Banks had $ 10 to $ 99.9 million. Specialist portfolios had (i) >$ 25M in credit card receivables per branch and fewer than 100 branches or (ii) yield greater than 30%. n=100.

    Credit Line Utilization

    The nuanced nature of portfolio management becomes even more apparent when credit line utilization is examined. Based on data from Figure 1, Accenture analyzed credit line utilization rates for a subset of 69 of in-scope FIs (excluding those portfolios with net charge-off rates in 2017 in excess of 5 percent and utilization outliers that imply a distinct product type). Credit line utilization was defined as credit card balances owed on transacting and revolving accounts divided by credit line commitments, inclusive of these balances, to extend credit to individuals for household, family and other personal expenditures through credit cards.

    Figure 2 shows significant dispersion of line utilizations by FIs with virtually no direct statistically correlative relationship at the portfolio level between credit line utilization and net charge-off rate, even when segmented by portfolio type.  At the cardholder level, one would anticipate credit line utilization to increase with net charge-off rates as FIs look to more closely manage credit lines for higher risk cardholders. And indeed, when customers are segmented within portfolio, we have observed portfolios to generally depict an inverse relationship between credit risk and line utilization.

    Although operational practices—and the soundness of those practices—may not always be visible without knowledge of the particular internal factors, the variance at a portfolio level may also reflect a wide array of approaches to credit line setting and ongoing account management observed in-market. These range from FIs that have halted proactive credit line increases ever since the global financial crisis, to those that are becoming more progressive in setting and revising credit lines, including through automated means of obtaining ability-to-pay information and cardholder-level multivariate decisioning. Together with the difference in portfolio dynamics and operational treatment, these variations in overarching strategy can have meaningful implications for contextualizing and evaluating performance.

    Figure 2:  Credit Line Utilization vs. Net Charge-Offs on Credit Cards

    The risk-returns roller coaster for US consumer credit cards fintech

    Source: Accenture analysis of FDIC call report data for US commercial banks, savings banks, and savings & loan associations with at least $ 10 million in consumer credit card balances as of year-end 2017, consumer credit line utilization rates ranging from 5% to 30%, and 2017 net charge-off rates on consumer credit card loans of up to 5%. National Banks had $ 10+ billion in credit card receivables for the period; Super Regional Banks had $ 1 to $ 9.9 billion; Regional Banks had $ 100 to $ 999.9 million; and, Community Banks had $ 10 to $ 99.9 million. Specialist portfolios had (i) >$ 25M in credit card receivables per branch and fewer than 100 branches or (ii) yield greater than 30%. n=69.

    Return on Equity

    Reflecting the full suite of drivers, including those above, and how issuers manage them, return on equity (ROE) figures for credit cards are typically both higher and more variable than other bank assets. Credit card banks—defined as FIs with at least 50 percent of total assets in consumer credit cards and which account for roughly half of the consumer card market—have a five-year running average ROE over double that of the banking industry average of 8.64 percent for 2017, per the US FDIC Quarterly Banking Profile for Fourth Quarter 2017.

    As alluded to above, return is not without risk. Although banks have been generally disciplined in requiring higher returns for riskier assets; the range of outcomes grows as charge-offs grow, magnified by leverage and real differences in strategies and operational capabilities. However, it is the combinations of these factors that not only make credit cards a challenging business, but also make them all the more rewarding over the long term for those banks that appreciate the variances in portfolio behavior and can manage the full suite of portfolio levers towards an overarching vision.

    Implications

    As a whole, the credit card industry is viewing today’s market as attractive for growth and providers are looking to outperform. With a healthy respect for the complexities of managing a card portfolio and appreciation of holistic interactions, leading FIs are clearly defining their business strategy, taking an integrated approach to portfolio management, and continually optimizing their business assets.

    The future always has elements of terra incognita and more so in today’s market. Unified approaches, facilitated by communication among the necessary parties across the cardholder lifecycle, can help individual issuers deliver portfolio performance improvements in the context of their credit card business vision, mission, risk tolerance and market conditions.

    For further reading, see how a major Brazilian financial services provider transformed its credit card processing and how a Latin American Bank used customer analytics to increase its credit card revenue.

     

    The post The risk-returns roller coaster for US consumer credit cards appeared first on Accenture Banking Blog.

    Accenture Banking Blog

     
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