The case for credit decisioning

Discussing the role of data management and governance in data decisioning doesn’t fall too far from what I’ve previously discussed about the issues that are still present in most organizations concerning data-driven decisions.

Almost every organization has impaired data dependent decision processes. There’s a variety of causes for this, just to give some examples:

• Data that is not enough to generate new insights.

• Data sources that are no integrated.

• Data that is not available on time.

• Data that has defects, errors, is missing or incomplete.

• Data that is not adequate to a business case.

• Data belonging to rogue data sets.

• Data that can’t be traced to source.

• Data generated by undocumented transformations.

Each of these problems seriously undermines the decision processes, making it hard for fully trusting the data that is made available for them to make decisions.

When putting this into the context of credit decisioning, or credit scoring, it’s easy to conclude that banks and other financial institutions (FIs) are exposed to serious risks due to some of these problems.

The present context has severely impacted the lending industry, creating a high volatile environment. With large numbers of customers facing financial difficulties, FIs are making decisions and planning actions that will have a severe and lasting impact on their performance and financial health.

This is an area where slow and unreliable decision processes are costly.

Customers (often in rapidly changing circumstances) are expecting immediate responses to on everything from loan approvals to credit-limit increases and account openings. Unless FIs are ready and able to process requests promptly, they risk losing revenue to competitors.

There is a rapid expansion of data-driven decision solutions. The capability to create predictive models from new and larger quantities of data is now easier through machine learning and other advanced analytics techniques. The potential benefits are considerable.

This context makes the adoption of data analytics, solutions an indispensable part of the credit decisioning process, pushing data into a more critical role and making Data Management and Data Governance key differentiators.

It is critical that FIs have an appropriate Data Governance framework in place, assuring standards, definitions, data quality metrics, reporting and, most importantly, accountability and ownership over the data, as this process must be business driven, not and IT driven, as the ownership and responsibility of data belongs to the business stakeholders.

FIs that can implement robust data governance frameworks – focusing on having the best quality data feeding the decision processes - will be able to succeed and extract maximum value from their data, and those that do not will fail.