As larger and increasingly complex stores of information are being gathered and maintained, Data Quality (DQ) often tends to slip. This is indicative of the fact that, while data collection and storing is crucial, it’s only the first step. For data to yield any benefit to an interested party, it must be turned into insightful information that is meaningful and understandable to the right set of people. When it comes to credit risk analysis, the stakes are even higher.
What is Credit Risk?
Credit risk is defined as the risks of loss that have the potential to occur if any party fails to function by the terms and conditions of a financial contract. Primarily, this focuses on the failure to pay off loans due to any lending entity. Any credit risk course worth its salt would attest to the fact that quality data is the foundation of credit risk analysis and mitigation.
Why Does Quality Data Matter?
When it comes to evaluating credit risk, the most critical task is to gather the necessary information for credit risk analysis and reporting in order to correctly. This is necessary to perform the appropriate assessment and review and to use this information efficiently to determine credit risk in order to prevent future losses.
To most industry players, the consistency of data forms the base of the systems dealing with credit risk analysis. It may come from an in-house database or from online outlets, such as websites for businesses. Alternatively, a company may buy performance ratings from a third-party provider for different markets or areas and can offer a detailed review based on fixed criteria.
Quality issues be eradicated at the earliest stage of the credit risk operation. Errors implemented during the credit risk appraisal stage, either due to input errors or a flawed compilation mechanism, will affect the organization on multiple levels– a risk no entity can afford.
Regulators, too, have become much stricter in their scrutiny, forcing risk management companies to pull up their socks and pay closer attention to data quality. Particularly, according to Moody’s, a greater emphasis is sought on data accuracy, traceability and granularity. Their scrutiny will also extend to the maintenance of central stores of data that is auditable.
Hiccups in the Quality Process
New data is continuously emerging, so quality checks must evolve with them. Data quality is not a one-off, open-shut process. It requires continuous tweaks to accommodate new data of differing kinds. With the advancements in technology and more interconnected cities, we might well see new platforms of data emerging, which ideally should mean new vetting processes.
Secondly, data will continue to diversify on many levels, from economic to geographic. Even existing data can be enriched by newly-found information, bringing in perspectives that could make or break credit risk management plans. While this does offer a much clearer picture of credit risk outlooks, it adds a further level of complexity to the system, which will need to be accounted for during quality assurance processes.
Operational Flows to Improve Data Quality
Checks must begin right from the data extraction stage, by verifying the authenticity of platforms used. Logical algorithms can be leveraged to profile these data dumps and create a picture of the overall data quality.
This is also the right stage to identify inaccuracies and define what to do with them. Erroneous data can be removed or modified as the analysts see fit. It’s also vital to identify duplicate data and remove them; allowing such data to pass through the sieve will likely create skewed results at a later stage.
Automated software and tracking algorithms can be created to execute this process over new data, weeding out incorrect occurrences and enriching existing data with new findings.
Accurate credit risk assessments and consequential plans are hinged on the quality of raw data. Therefore, it is imperative that risk-prone organizations collect data from authentic sources and service the right kind of information from the data dumps.