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Risky Decisions Result From Insufficient Data

May 2, 2016 | stratabeatit

The amount and type of data we generate in our everyday lives has changed beyond measure with the technology revolution. 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, companies want to make risk decisions quickly and reliably. For this, they rely on data and sophisticated integration tools to access it.

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. By automating the discovery, analysis and integration of this information into decision-making processes, financial institutions can help safeguard against certain types of risk.

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. Travellers 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. Financial services is comparatively late in tapping rich, new data sets.

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 recognised 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.

With so much data available, and with it taking non-standardised 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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