Risky Credit Decisions Result From Insufficient Data

Author: Paul Thomas

When I think about credit risk scoring and what lenders are trying to do when they look at applications, there’s one phrase that always comes to mind:

The devil is in the details

Now, why is that I hear you ask?

While the origin of the phrase is still up for debate, I think we can all agree on its meaning, which translates to something along the lines of:

A friend suggests a quick bicycle ride to the pub…

but fails to mention that the forecast says torrential rain, the route crosses three highways, there’s a field of bulls to navigate through, and it takes about 5 hours.

Basically, ‘the devil in the details’ is reminding you that the real story is often in the hidden details or, the risk is in the data.

Seems obvious, right? Absolutely, but knowing what you need to look for and actually being able to do it are two very different things.

The world is generating more ‘details’ than ever before

The amount and type of data we generate in our everyday lives has changed beyond measure with the technology revolution. There is an infinite amount of data out there; it’s widely available, updates all the time and takes modern-day forms. For example, in the modern digital world, trends become evident through online review sites and other crowd-sourced platforms long before they show up in financial reports. Customer reviews or customer complaints provide a good indication of how a company is performing, and from this, an assessment can be made on how that business is likely to fare in the next month, six months, a year.

Yet, data used by financial institutions has remained largely unchanged and hinges upon traditional, largely historical financial sources such as credit history. To meet today’s customer need for speed, innovative companies who want to make risk decisions quickly and reliably are looking at more diverse data sources.

Accessing and analyzing the devil in the details/risk in the data takes time, or does it?

What often stands between financial services organizations and the data they need to make smarter decisions is the ability to access and analyze data quickly and efficiently. They’re held back by data source integration delays or waiting for overworked dev teams to implement risk models. But many innovative companies are changing the way that their risk teams manage risk strategy by empowering businesses users to take more control over the process. For this, they rely on data and sophisticated integration tools to access it. By automating the discovery, analysis, and integration of this information into decision-making processes, financial institutions and fintechs can help safeguard against certain types of risk.


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More data equals more details

Data drives decisions. To deliver rapid, efficient credit and loan services, financial institutions want to capture a minimal amount of information from customers upfront and let data drive the process. An end-to-end scalable solution is completed with the addition of analytics and process management tools. It is commonplace today to consult review sites and Twitter to help determine if a company is trustworthy or provides good service. Travelers read reviews on TripAdvisor before they book. In the corporate world, data mining tools ping marketing departments the latest comments on their company from social media. Yet, financial services is comparatively late in tapping rich, new data sets.

Using data to improve access to credit

Many small businesses struggling to secure funding would welcome the opportunity for recent customer reviews to have an influence on their assessment for credit. They may argue that this feedback provides a more genuine, up-to-date view of where the business is, how it is performing and where it is going. It could certainly paint a customer-centric foreground onto the company’s financial background.

Difficulties in identifying, gathering data on and scoring some small or start-up businesses can hamper the decisions of bank and non-bank lenders. It’s a recognized problem with some initiatives being introduced to help more small businesses get the funding they need.

It works both ways, of course. Existing data sources may confer a positive score on a business while online activity could raise red flags and indicate a level of risk the lender would wish to protect itself from.

What this means for financial services organizations

With so much data available, and with it taking non-standardized forms, integrating it into structured business risk analytic models can be a challenge. Data discovery, analysis, and integration has to be automated; if it adds manual elements to the overall process its inclusion would drag out rather than enhance overall risk analytics and decisioning. Ultimately, the lender or credit provider needs to retain control of the process, whichever data it chooses to use.


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