Analytics Value Across the Credit Lifecycle

As lending markets continue to change and new generations of borrowers hit prime earning years, the financial landscape continues to shift. Government oversight has swiftly intensified, regulations are tightening and lending has become exponentially more complex. 

Fintech and alternative lending options are in growth mode, in large part due to the fact that loans to small businesses are currently struggling with less collateral or reduced cash flow, making them appear less creditworthy. 

Without strong analytics strategies across the whole credit lifecycle, it is impossible to fully optimize a lending portfolio. With analytics in place, high quality leads can be secured, leading to better origination. Lenders can enter and maintain a growth cycle, and manage collections internally if required. 

The Credit Lifecycle

The lifecycle of credit risk management is continual. It revolves around the four phases of lead buying, loan originations, account management, and collections – before the process begins again with a new offer to existing customers in good standing completing the loop.

Analytics can be the key to streamlining and modernizing your credit risk management lifecycle. Since lenders must be proactive and flexible while remaining well-organized and disciplined, creditworthiness  can’t be summed up in an initial approval. Vigilance must be employed to mitigate and limit fraud, and a finger kept on the pulse of borrowers’ health. 

Lead Buying 

Prospecting, or lead buying, is the start of the credit lifecycle. By applying analytics, lenders can acquire the right leads based on their decisioning rules and policies, and achieve the highest possible conversions.

COVID effectively “turned off” lead buying engines as temporary rules and policies came into play. Now that engines are “turned on” again, it’s vital to accurately evaluate both consumers and SMBs. 

Preapprovals are often not sufficient for accurate lead-buying. If 500 preapproved leads only yield 10 actual approvals during the decisioning process, the returns may not be sufficient to cover the 490 bad leads paid for – and certainly won’t maximize revenues.

Evaluating potential borrowers is a critical point in the credit lifecycle. Verifying potential customers is the best way to help minimize fraud. The gold standard for evaluating small businesses is blending analysis of the borrower’s company data and their personal financial and credit history, to appropriately assign risk tolerance levels.  

Loan Origination 

Accurate decisioning to drive loan origination requires the right models, the right analytics, and the right sources. Having the best, most current, and most relevant data available at all times allows instant decisioning power to achieve peak performance for your portfolio.

Providing loan origination officers and decisioning engines with the optimized data decisions capabilities helps them accurately identify, target, and evaluate the best candidates among loan applicants and surface their applications for processing.

Account Management  

Ideally, borrowers will require little or no management, and most actions can be automated to keep customer relationships running smoothly. If contact is required, action can be informed by predictive intelligence using algorithms that analyze real-time data from multiple sources to de-duplicate data and increase accuracy for closer customer tracking.

Bank transaction data can be invaluable to evaluating the ongoing credit-worthiness of a loan customer. Increases in deposits or average daily balances can indicate a raise in income. Decreases, or cessation of a direct deposit can indicate the loss of a job. Consistently low or overdrawn bank accounts speak to cash flow problems or new debt.

Continual analysis allows lenders to continually manage accounts in a profitable manner. Various alerts can be set up to prompt offers for different lines, such as insurance or an auto loan. Data can indicate if a consumer is ready to be introduced to a new product in the portfolio, or be leveraged as a pre-collections trigger by identifying consumers in trouble.

Collections

Predictive analytics can provide a scoring system for debtors that helps collections agents prioritize their workload and bring late- or no-pay customers into good standing. This can stabilize and strengthen your bottom line while simultaneously allowing you to gather data that can tighten your prospecting and decisioning models.   

Agents can use analytics to prioritize accounts and build a decision tree. If one client doesn’t pay at 30 days, but always pays by 45 days if a text is sent, it’s a low risk account that can likely be handled via automation. Consider adding a reminder text before the 30 day mark to help them improve their payment cycle and avoid late fees. If another credit customer is standing at 60 day, but has never been late before, hand the case off to a junior collector. Seriously delinquent accounts that have been unresponsive to outreach can be bumped up to a senior collections agent.   

Giving front-line officers timely, accurate data will help them interpret events in tandem with credit-review teams to proactively address concerns over accounts, whether that is lowering a credit limit if a borrower is going into collections with other lenders, or closing an account entirely if a bankruptcy is declared.  

GDS Link

GDS Link specializes in risk management software and analytics solutions for banks, credit unions, and alternative lenders, delivering analytics through software spanning the entire lifecycle. From lead buying through loan origination and decisioning to underwriting and fraud prevention, we deliver the solutions you need to attract and evaluate borrowers with increased accuracy.

In our latest Case Study, GDS Link’s Analytics Team worked with a client known for being a pioneer within consumer lending, and helped grow their lending volume 400%. Click below to see how we did it. 

 

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