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  • @fintechna 3:35 am on May 14, 2018 Permalink | Reply
    Tags: , , , , coaster, , , ff0000, 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

    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

    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.


    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

  • @fintechna 6:38 pm on October 26, 2017 Permalink | Reply
    Tags: , , , , , ff0000, FutureReady, , zerobased   

    Target black box costs for zero-based, Future-Ready Banking 

    Seneca, the 1st-century Roman statesman, didn’t believe in luck. For him, what others called luck was when opportunity met preparation. Hopefully there aren’t many bank CEOs relying on luck to get them through their digital transformations. The opportunity in front of them is becoming clearer—to thrive in a more open and competitive industry by being customer-centric and agile—but what of the preparation? What does a Future-Ready Bank look like? One big obstacle that need to tackle is their cost base; particularly, seeing and understanding “ box” and then assigning ownership of them in ways that provide competitive advantage.

    Regardless of how committed a management team is to becoming Future-Ready, in the words of Muhammad Ali, “The hands can&;t hit what the eyes can&8217;t see.”

    The black box refers to the complex and opaque costs, functions, processes and activities in banks that are not directly related to any single line of business. Comprising some 65 percent of a bank’s cost base, the complexity, centralisation, disparate data and non-accountability of the black box lands a knockout punch to bank profitability and evolution.

    Banks can gain visibility into black box costs by bringing together data from the General Ledger, HR and AP systems, invoices, and other data sources to create a rich dataset that categorises costs in a meaningful way and clarifies cost ownership. It provides management with a front-to-back value chain view of the organisation, costs and headcounts tied directly to business line and revenue base.

    Once banks have a clear view on this hefty share of their costs, they can assign responsibility for most, if not all of the cost base. Bank leadership can make such ownership concrete by creating a framework for rewarding managers based on successful cost management. Arguably, clarifying cost ownership represents the greatest shift in improving a bank’s ability to manage itself.

    Read the report

    With visibility in hand and ownership in place, banks are positioned to better re-enact zero-based approaches to get off the traditional ropes and transform to the “new”. They can challenge not only the cost, but also if the activity needs to be done in the first place—informed by actionable, granular-level data analysis on how cost, risk and capital interact, to then purge unwarranted activities.

    Though not a new concept, zero-based budgeting is becoming more critical as rates and yield curves rise, compliance costs increase and new agile, digital-native contenders emerge. Rather than being boxed into a cost corner, banks can fundamentally rethink their path to efficiency, better their cost-income ratios and, ultimately, their digital readiness. It requires banks to establish a culture in which visibility, transparency, simplicity and ownership of costs are the goals of the organisation. With the right preparation, banks will be able to make their own luck in a digital future.

    To learn more, I invite you to read our report: Get fit | Shining a light into the black box


    The post Target black box costs for zero-based, Future-Ready Banking appeared first on Accenture Banking Blog.

    Accenture Banking Blog

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