All posts by Andrew Beddoes
There are lots of reasons why people miss a bill payment. Unfortunately, the approaches for collecting those late payments tend to follow a one-size-fits-all approach. For example, a customer takes a long vacation and forgets to pay his credit card bill before he leaves. When he gets home ten days later he already has five phone calls and two official notices from the collections department. And the calls continue for the next few days until he has an opportunity to call them back to take care of the situation. How does this customer feel? Frustrated? Annoyed? Under-valued? The current collections process is outdated, especially in a world where personalized and relevant communications are becoming the norm. The collections process is driven by the measurement of delinquency and loss. Rarely does it consider the broader customer profile. Too often, this narrow view leads to one-size-fits all collections strategies and overly aggressive processes. Getting debt collection right is about more than the money. It needs to be about the customer. It’s about knowing the difference between a customer who has simply forgotten to make a payment or someone dealing with financial hardship. We recently released a paper on the collections process, looking at how common it is for overly aggressive collections to lead to account closures and erosion of customer lifetime value. In fact, we found 3 percent of 30-day delinquencies in card portfolios closed their accounts after paying their balance in full. Seventy-five percent of those closures came with the payment to bring the account current and the remaining 25 percent closed the account within the following 60 days. Our analysis also showed that people with the lowest balances and the lowest amount past-due had the highest incidence of paying off their debt and closing their accounts. That means that by being too aggressive over a fairly low dollar amount, you can damage the relationship with a customer who could bring a lifetime of more business. We also used our Mosaic® lifestyle segmentation system to do a deeper look into who was more likely to close their accounts. We found the likelihood to close accounts was four times higher in the young, urban, affluent population than it was with others. This particular segment usually has the greatest potential for lifetime value and are the hardest to attract. It seems counterproductive to let overly aggressive and perhaps unnecessary, debt collection end the relationship. However, if we become smarter about the collections process, it could be possible to not only keep the customer, but also strengthen the relationship. Consider this, the customer on that long vacation receives an email that says, “Hey we noticed you forgot to make your last payment. Can you email us back the reason why and when we should expect it?” This creates an opportunity to build the relationship with that customer and get a commitment to settle the payment by a particular date. Ten days later when he gets back from vacation, he receives a reminder to make the payment and takes care of it. Instead of feeling frustrated, annoyed or undervalued, the customer has a positive experience and continues doing business with a company who is focused on building relationships vs. simply collecting money. There is a real opportunity to take best practices around customer experience from earlier stages of the customer lifecycle and apply them to the collections stage. Sure, some situations may require a more aggressive approach, for others, the idea of an email and the option to log on to a virtual platform to handle the debt on their own terms is the preferred approach. Some customers may not need options other than a little more time to pay on their own terms. It comes down to knowing your customer and applying the insights we can uncover from data to handle debt collection. This will allow us to improve the customer experience and strengthen customer life-time value.
Understanding the behaviors of best-in-class credit risk managers For financial institutions to achieve superior performance, having the appropriate set of credit risk managers is a prerequisite. The ability to gain insight from data and customer behavior and to use that insight for strategic advantage is a critical ingredient for success. At the same time, the risk-management community is under increasing pressure to understand and explain underlying trends in credit portfolios — and to monitor, interpret and explain these trends with ever-greater accuracy. A common problem financial institutions face when confronting staff resource needs is the difficulty in recruiting and retaining experienced risk-management professionals. The risk-management community is notoriously small, and hiring expertise from within this community is extremely difficult. Skilled risk managers truly are a finite resource, but their skill set is in huge demand. Hiring the right talent is crucial to job satisfaction, leading to higher engagement levels and reduced attrition costs. On top of that, employee engagement is vital to an organization’s success. It drives employee productivity and fosters a culture of innovation, which leads to higher profitability for the entire organization. Building, attracting and retaining risk-management resources requires a commitment to engaging in staff personal development. A great way to support employee engagement is to invest in their personal and professional development, including opportunities for training and team building. If an organization can show that it is committed to developing its people and providing opportunities for career growth, employee engagement levels will rise, with all the benefits this entails. Typically, financial institutions bridge the resource skill gap by either hiring skilled statistical and analytical experts or developing in-house resources. Both of these approaches, however, require significant on-the-job training to teach employees how to link raw statistical techniques and procedures to influencing the profit and loss statement of the business line which they support. The challenge is often broadening the understanding of these skill set “silos” and their contribution to the overall portfolio. By opening that view, the organization generates additional value from these resources as lines of communication are improved and insights and opportunities found within the data are shared more effectively across the organizational team. Experian’s Global Consulting Practice provides a solution to this problem. Our two-day Risk and Portfolio Management Essentials training workshop offers the opportunity to understand the behaviors of best-in-class risk managers. What are the tools and enablers required for the role? How do they prepare for the process of managing credit risk? What areas must risk managers consider managers across the Customer Life Cycle? What differentiates the good from the great? To complement the training modules, Experian® offers an interactive, team-based approach that engages course participants in the build options of a defined portfolio. Participants leverage the best-in-class techniques presented in the sessions in a series of competitive, team-based exercises. This set of cross-organizational exercises drives home the best-in-class techniques and further builds understanding that resonates across the organization long after the course is concluded. For our current offerings, locations and to register click here.
The desire to return to portfolio growth is a clear trend in mature credit markets, such as the US and Canada. Historically, credit unions and banks have driven portfolio growth with aggressive out-bound marketing offers designed to attract new customers and members through loan acquisitions. These offers were typically aligned to a particular product with no strategy alignment between multiple divisions within the organization. Further, when existing customers submitted a new request for credit, they were treated the same as incoming new customers with no reference to the overall value of the existing relationship. Today, however, financial institutions are looking to create more value from existing customer relationships to drive sustained portfolio growth by increasing customer retention, loyalty and wallet share. Let’s consider this idea further. By identifying the needs of existing customers and matching them to individual credit risk and affordability, effective cross-sell strategies that link the needs of the individual to risk and affordability can ensure that portfolio growth can be achieved while simultaneously increasing customer satisfaction and promoting loyalty. The need to optimize customer touch-points and provide the best possible customer experience is paramount to future performance, as measured by market share and long-term customer profitability. By also responding rapidly to changing customer credit needs, you can further build trust, increase wallet share and profitably grow your loan portfolios. In the simplest sense, the more of your products a customer uses, the less likely the customer is to leave you for the competition. With these objectives in mind, financial organizations are turning towards the practice of setting holistic, customer-level credit lending parameters. These parameters often referred to as umbrella, or customer lending, limits. The challenges Although the benefits for enhancing existing relationships are clear, there are a number of challenges that bear to mind some important questions to consider: · How do you balance the competing objectives of portfolio loan growth while managing future losses? · How do you know how much your customer can afford? · How do you ensure that customers have access to the products they need when they need them · What is the appropriate communication method to position the offer? Few credit unions or banks have lending strategies that differentiate between new and existing customers. In the most cases, new credit requests are processed identically for both customer groups. The problem with this approach is that it fails to capture and use the power of existing customer data, which will inevitably lead to suboptimal decisions. Similarly, financial institutions frequently provide inconsistent lending messages to their clients. The following scenarios can potentially arise when institutions fail to look across all relationships to support their core lending and collections processes: 1. Customer is refused for additional credit on the facility of their choice, whilst simultaneously offered an increase in their credit line on another. 2. Customer is extended credit on a new facility whilst being seriously delinquent on another. 3. Customer receives marketing solicitation for three different products from the same institution, in the same week, through three different channels. Essentials for customer lending limits and successful cross-selling By evaluating existing customers on a periodic (monthly) basis, financial institutions can assess holistically the customer’s existing exposure, risk and affordability. By setting customer level lending limits in accordance with these parameters, core lending processes can be rendered more efficient, with superior results and enhanced customer satisfaction. This approach can be extended to consider a fast-track application process for existing relationships with high value, low risk customers. Traditionally, business processes have not identified loan applications from such individuals to provide preferential treatment. The core fundamentals of the approach necessary for the setting of holistic customer lending (umbrella) limits include: · The accurate evaluation of credit and default risk · The calculation of additional lending capacity and affordability · Appropriate product offerings for cross-sell · Operational deployment Follow my blog series over the next few months as we explore the essentials for customer lending limits and successful cross-selling.
There are two core fundamentals of evaluating loan loss performance to consider when generating organic portfolio growth through the setting of customer lending limits. Neither of which can be discussed without first considering what defines a “customer.” Definition of a customer The approach used to define a customer is critical for successful customer management and is directly correlated to how joint accounts are managed. Definitions may vary by how joint accounts are allocated and used in risk evaluation. It is important to acknowledge: Legal restrictions for data usage related to joint account holders throughout the relationship Impact on predictive model performance and reporting where there are two financially linked individuals with differently assigned exposures Complexities of multiple relationships with customers within the same household – consumer and small business Typical customer definitions used by financial services organizations: Checking account holders: This definition groups together accounts that are “fed” by the same checking account. If an individual holds two checking accounts, then she will be treated as two different and unique customers. Physical persons: Joint accounts allocated to each individual. If Mr. Jones has sole accounts and holds joint accounts with Ms. Smith who also has sole accounts, the joint accounts would be allocated to both Mr. Jones and Ms. Smith. Consistent entities: If Mr Jones has sole accounts and holds joint accounts with Ms. Smith who also has sole accounts, then 3 “customers” are defined: Jones, Jones & Smith, Smith. Financially-linked individuals: Whereas consistent entities are considered three separate customers, financially-linked individuals would be considered one customer: “Mr. Jones & Ms. Smith”. When multiple and complex relationships exist, taking a pragmatic approach to define your customers as financially-linked will lead to a better evaluation of predicted loan performance. Evaluation of credit and default risk Most financial institutions calculate a loan default probability on a periodic basis (monthly) for existing loans, in the format of either a custom behavior score or a generic risk score, supplied by a credit bureau. For new loan requests, financial institutions often calculate an application risk score, sometimes used in conjunction with a credit bureau score, often in a matrix-based decision. This approach is challenging for new credit requests where the presence and nature of the existing relationship is not factored into the decision. In most cases, customers with existing relationships are treated in an identical manner to those new applicants with no relationship – the power and value of the organization’s internal data goes overlooked whereby customer satisfaction and profits suffer as a result. One way to overcome this challenge is to use a Strength of Relationship (SOR) indicator. Strength of Relationship (SOR) indicator The Strength of Relationship (SOR) indicator is a single-digit value used to define the nature of the relationship of the customer with financial institution. Traditional approaches for the assignment of a SOR are based upon the following factors Existence of a primary banking relationship (salary deposits) Number of transactional products held (DDA, credit cards) Volume of transactions Number of loan products held Length of time with bank The SOR has a critical role in the calculation of customer level risk grades and strategies and is used to point us to the data that will be the most predictive for each customer. Typically the stronger the relationship, the more we know about our customer, and the more robust will be predictive models of consumer behavior. The more information we have on our customer, the more our models will lean towards internal data as the primary source. For weaker relationships, internal data may not be robust enough alone to be used to calculate customer level limits and there will be a greater dependency to augment internal data with external third party data (credit bureau attributes.) As such, the SOR can be used as a tool to select the type and frequency of external data purchase. Customer Risk Grade (CRG) A customer-level risk grade or behavior score is a periodic (monthly) statistical assessment of the default risk of an existing customer. This probability uses the assumption that past performance is the best possible indicator of future performance. The predictive model is calibrated to provide the probability (or odds) that an individual will incur a “default” on one or more of their accounts. The customer risk grade requires a common definition of a customer across the enterprise. This is required to establish a methodology for treating joint accounts. A unique customer reference number is assigned to those customers defined as “financially-linked individuals”. Account behavior is aggregated on a monthly basis and this information is subsequently combined with information from savings accounts and third party sources to formulate our customer view. Using historical customer information, the behavior score can accurately differentiate between good and bad credit risk individuals. The behavior score is often translated into a Customer Risk Grade (CRG). The purpose of the CRG is to simplify the behavior score for operational purposes making it easier for noncredit/ risk individuals to interpret a grade more easily than a mathematical probability. Different methods for evaluating credit risk will yield different results and an important aspect in the setting of customer exposure thresholds is the ability to perform analytical tests of different strategies in a controlled environment. In my next post, I’ll dive deeper into adaptive control, champion challenger techniques and strategy design fundamentals. Related content: White paper: Improving decisions across the Customer Life Cycle
Last January, I published an article in the Credit Union Journal covering the trend among banks to return to portfolio growth. Over the year, the desire to return to portfolio growth and maximize customer relationships continues to be a strong focus, especially in mature credit markets, such as the US and Canada. Let’s revisit this topic, and start to dive deeper into the challenges we’ve seen, explore the core fundamentals for setting customer lending limits, and share a few best practices for creating successful cross-sell lending strategies. Historically, credit unions and banks have driven portfolio growth with aggressive out-bound marketing offers designed to attract new customers and members through loan acquisitions. These offers were typically aligned to a particular product with no strategy alignment between multiple divisions within the organization. Further, when existing customers submitted a new request for credit, they were treated the same as incoming new customers with no reference to the overall value of the existing relationship. Today, however, financial institutions are looking to create more value from existing customer relationships to drive sustained portfolio growth by increasing customer retention, loyalty and wallet share. Let’s consider this idea further. By identifying the needs of existing customers and matching them to individual credit risk and affordability, effective cross-sell strategies that link the needs of the individual to risk and affordability can ensure that portfolio growth can be achieved while simultaneously increasing customer satisfaction and promoting loyalty. The need to optimize customer touch-points and provide the best possible customer experience is paramount to future performance, as measured by market share and long-term customer profitability. By also responding rapidly to changing customer credit needs, you can further build trust, increase wallet share and profitably grow your loan portfolios. In the simplest sense, the more of your products a customer uses, the less likely the customer is to leave you for the competition. With these objectives in mind, financial organizations are turning towards the practice of setting holistic, customer-level credit lending parameters. These parameters often referred to as umbrella, or customer lending, limits. The challenges Although the benefits for enhancing existing relationships are clear, there are a number of challenges that bear to mind some important questions: How do you balance the competing objectives of portfolio loan growth while managing future losses? How do you know how much your customer can afford? How do you ensure that customers have access to the products they need when they need them What is the appropriate communication method to position the offer? Few credit unions or banks have lending strategies that differentiate between new and existing customers. In the most cases, new credit requests are processed identically for both customer groups. The problem with this approach is that it fails to capture and use the power of existing customer data, which will inevitably lead to suboptimal decisions. Similarly, financial institutions frequently provide inconsistent lending messages to their clients. The following scenarios can potentially arise when institutions fail to look across all relationships to support their core lending and collections processes: Customer is refused for additional credit on the facility of their choice, whilst simultaneously offered an increase in their credit line on another. Customer is extended credit on a new facility whilst being seriously delinquent on another. Customer receives marketing solicitation for three different products from the same institution, in the same week, through three different channels. Essentials for customer lending limits and successful cross-selling By evaluating existing customers on a periodic (monthly) basis, financial institutions can holistically assess the customer’s existing exposure, risk and affordability. By setting customer level lending limits in accordance with these parameters, core lending processes can be rendered more efficient, with superior results and enhanced customer satisfaction. This approach can be extended to consider a fast-track application process for existing relationships with high value, low risk customers. Traditionally, business processes have not identified loan applications from such individuals to provide preferential treatment. The core fundamentals of the approach necessary for the setting of holistic customer lending (umbrella) limits include: The accurate evaluation of credit and default rise The calculation of additional lending capacity and affordability Appropriate product offerings for cross-sell Operational deployment Follow my blog series over the next few months as we explore the core fundamentals for setting customer lending limits, and share a few best practices for creating successful cross-sell lending strategies.
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