Experian offers insight for asset-lending CFOs using Big Data

Feelings of uncertainty

To add to this feeling of uncertainty, growth is subpar; leading some prominent economists to suggest the US is in a period of “secular stagnation.” Government oversight has also intensified. The biggest banks must now have chief risk officers and risk committees on boards. Examiners question both big losses and large gains as signs of excessive loan risk-taking.

As a result of all of these factors, lenders want bulletproof applicants. With economic anxiety lingering, standards remain tough. This is particularly troubling for small businesses: loans to smaller companies are off 18% from the 2008 high of $711 billion, according to the FDIC (Federal Deposit Insurance Corporation).

While banks have since loosened standards for larger companies, loans to small businesses (historically the creators of two out of every three net new jobs) lag partly because these firms were hit harder by the recession. For many smaller companies this has translated into less collateral or reduced cash flow, making them less creditworthy. Further, transaction costs to process loans to companies are the same regardless of size, and smaller loans offer less profit and more risk.

Unlocking growth in challenging times

The never-ending lifecycle of credit risk management revolves around the interconnected phases of prospecting, decision-making (the acquisition of new business), and relationship management with the goal of minimizing collections.

Throughout this cycle, lenders face challenges. To successfully manage the process, lenders must be proactive, flexible (yet have well-organized and disciplined systems), and alert for fraud or declines in borrowers’ health.

Senior loan officers have many moving parts to manage during the lending process, including analytics, modeling, underwriting consistency, process refinement, upselling, and retention. With so many moving pieces to master, lenders also need to maintain focus on the big picture.

The best approach means understanding the issues, opportunities, and risks in each of the life cycle’s four phases— prospecting, acquisition, management, and collections.

Overcoming credit management inefficiencies

Today lenders must dig deeper on the front end to uncover what’s behind the numbers. With the costs of acquiring new customers higher than ever and ideal customers fewer and far between, sophisticated lenders increasingly rely on propensity models. These models help them to actively search out and dissect new pools of potential prospects which fit the needs of their existing loan segmentations.

If big data can be used to crack the human genome, then by all means financial executives must use it to crack open the financial secrets of loan seekers.

Lenders are now creating models comprised of standard business performance data, and even online business reviews to develop predictive formulas. While some social media data can be unreliable, volatile, or manipulated by competitors posting phony negative reviews, such information is being increasingly exploited, if cautiously. With further refinements, it is likely to become a permanent and commonplace part of comprehensive loan evaluations.

Action-oriented, aggressive prospecting is well on its way to being a key element in growing every lender’s portfolio. The bottom line is that the more information a lender can speedily and accurately process, the better the potential outcome. The beauty of such advanced data and analytics is that all lenders—from the largest global entity to smaller regional ones—can mine the same financial gold fields.

Prospecting: New tools for evaluating potential borrowers

Today’s “gold standard” requires blending an analysis of the borrower’s company and his personal credit and financial history. Reliance on such blended information makes it far more likely to ferret out whether a potential borrower’s business has been deteriorating. It also helps to unmask potential fraudster by revealing discrepancies in a loan-seeker’s personal background.

Relying on multiple data sources will help meet compliance requirements for such regulations as the Red Flag Rule and the USA PATRIOT Act and serve as part of any lender’s anti-money laundering surveillance and compliance. By using targeted analytics, you can assign your risk-tolerance levels, simplify and streamline how you treat each applicant, and ultimately make more productive use of your time and that of your analysts.

Acquisition: using blended analysis to make the decision to lend

Often the biggest problem lenders face is uncertainty over when to act, what specific actions to take, or even information overload. Should a lender reduce a credit line? If so, by how much? Or should the account be closed entirely?

Overreacting can be as risky as inaction. The best solution is to give front-line loan officers the best possible, most timely data so that they can interpret events correctly in coordination with credit-review teams and senior management to proactively adjust the limits and terms of an account. For prudent decision-making no substitute exists for multiple levels of personal oversight by seasoned financial professionals.

Ongoing portfolio oversight systems that can be fashioned to fit the needs various borrowers are growing more popular. These timely and actionable systems can be accessed by various users in a lender’s oversight team. The system can give the lender a competitive edge if it triggers a warning they’ve set up to show a borrower is struggling, perhaps through defaulting on a credit obligation, going into collections with other lenders, or even declaring bankruptcy.

Management: new systems to gain an edge on oversight

Thinking about Collections might not inspire much positivity, but there is good news. New developments are transforming the once tedious and inefficient collections process.

In recent years the availability of big data has made it possible for businesses to push a button and instantly get an avalanche of information about a person. Even so, collections companies still manually cut-and-pasted data into files. Old, core problems persisted; there was simply too much data, much of which was wrong, duplicative, or irrelevant.

Today Collections is undergoing a revolution that’s dramatically streamlining the process. The key? Predictive analytics or, perhaps better put, predictive intelligence.

Algorithms analyze real-time data from multiple sources (such as credit bureaus and credit data record providers) with the goal of predicting future outcomes. On the most basic level, predictive analytics threshes out multiple entries, de-duplicating data. It’s also very perceptive—calculating and recalculating the accuracy of data in real-time based on historical information about thousands of people’s behavior.

Collections: predictive resolutions to problems

Senior finance executives today have more tools than ever to build and strengthen existing loan portfolios while also aggressively seeking out worthy, new loan candidates. Today’s credit risk officers have what it takes to round up and drive robust herds to market. While they may not have lassos, today’s financial tools hit the bull’s eye equally well.

Gaining Efficiencies & Access: finance & the credit lifecycle by Experian Business Information Services