In this blog, we explore how BRMSs are used across the customer lifecycle.
The acquisition phase incorporates the prospecting for new customers. Once data is obtained on a candidate list, models can be applied to the data to understand the likelihood of response to a campaign offer, the risk, the underlying probability of attrition and perhaps the overall profitability.
The data can then be sent down a waterfall decision-tree to identify those who should be targeted.
As well as managing the selection. The channel and messaging of these customers should also be managed too. A good BRMS should allow you to test communication channels, messaging type, the right time to contact, etc. For clean tests, randomisation should be used.
Read more about Randomisation in the Credit Lifecycle here.
As data-sourcing, analytical modelling techniques and legislative requirements increase and become more complex, the demand for sophistication in a decision engine grows. This is particularly true in originations where a variety of decisions and calculations are required.
Of similar importance is flexibility. Changes to strategy and calculations should be in the hands of business and not IT. For the originations lifecycle, you need a scoring module, matrices and decision trees to combine scores, calculations executed (e.g. affordability, limit calculations, premium values or contract tariffs).
In credit, a critical component in account management strategies is the customer’s behavioural score. The behavioural score incorporates a variety of information (most notably balances, limits, repayments, delinquency statuses) from typically the last 12 months. The score produced indicates a probability of moving to three cycles delinquent in the next 6/12 months.
A good BRMS should allow for the scoring of behavioural data to produce a behavioural score to be used in account management and account provision calculations.
Management of credit limits
One of the biggest drivers of profitability within a retail revolving credit business is through effective management of credit limits. Data-driven strategies should be built to help tier credit-limit offers to ensure that appropriate limits are given to suitable account holders. Through a combination of scorecards, matrices, decision trees, and outcome tables a final % limit increase offer is determined within a BRMS. The final limit offer amount can be calculated based on limit, balance, percentage increase, the maximum limit, minimum incremental increase, etc.
All strategies should be implemented on a champion/challenger basis.
The resulting limit increase offer is typically fed back to the host system that will then send out offers via the various channels.
Although a simple “authorisation for the month” strategy (run alongside the monthly CLM batch) is common, most bank credit cards run more sophisticated authorisations. Authorisations should complement credit limit management strategies by offering customers both a “fit” and “cushion” limit and potentially offering a holiday option too.
For authorisations, at a point-of-sale device, a transaction will be identified as pushing the customer above their credit limit. A call to the host system that in turn calls the BRMS is required.
Using the scoring engine, decision trees, authorisation table and calculations, a BRMS determines the “fit” and “cushion” percentages. This will be fed back to the POS to approve or decline the transaction.
Customer engagement strategy
Understanding your customers through analytics enables a company to position an up-sell/cross-sell at the right time. Personalised actions can also be used to stimulate specific actions from the customer. Customer engagement to reduce attrition, grow good balances and re-activate dormant customers are common. A BRMS can offer scoring, segmentation, clustering and action tables for customer engagement.
If transactional data is used, then more advanced engagements utilising “next-best-product” are also available.
Given the importance of a single point of truth and the awareness of product holdings for cross-sell purposes across the customer base, a BRMS ideally requires a level of flexibility and control.
Customer segmentation in collections is a critical requirement whether it’s determining which accounts to target for a pre-delinquency campaign, prioritising accounts in early and late-stage collection and managing the legal process.
Customer segmentation is done through a combination of scoring and segmentation. The key segments are then allocated treatments also managed through a BRMS.
Additional tools can include:
Right-time-to-call (RTTC) models enable call centres to improve their right-person connect rates for collections. This should be deployable within a BRMS. Collections prioritisation should incorporate risk, balance and contactability.
Your BRMS can be used to manage the out-sourcing of books to External Debt Collectors.
The allocation of an optimal settlement discount based on propensity to pay and indebtedness can assist a collection manager offer settlement most appropriate for each debtor. A BRMS can assist in passing maximum settlement discounts to assist in the collector’s negotiation process.
Monthly provisions/capital calculations
As a monthly compliance requirement, credit granting institutions require the calculation of monthly provisions. In addition to capital regulatory, capital calculations are also required to be produced for a bank.
What this typically means is that organisations need an operational process whereby data is extracted from the host system, models and segmentation are applied to each account, the models are combined to obtain an expected loss (IFRS9) or risk-weighted asset (Basel). The provided amount is then reported on.
Read more on IFRS 9 for Retail Lending here.
A good BRMS system should allow for the deployment of these models (typically behavioural scores, PD, LGD, EAD), the running of calculations and the production of reports. Data should also be extractable that will allow for monitoring of the models.
A part of the collections process is to know when to sell your non-performing accounts. By running a payment projection score on these accounts and understanding the collections costs, one can easily identify the candidate accounts for debt sale as well as a “fair price”.
A BRMS can assist in managing these models. For sophisticated books, multiple models may be used not just looking at the “average” that’s likely to be collected, but “best case” and “worst-case” valuations.
At Principa we’ve built our BRMS, DecisionSmart, with business applications in mind. Currently, our software is deployed across multiple clients who use it for a variety of different applications across the customer lifecycle. Learn more about DecisionSmart here.