Banks Should Convert Unstructured Mortgage Loan Data to Prevent Frauds

Banking and finance, has been among the very first few industries that realized the worth of data and adopted data-driven policy. Investment Banks, Regional Banks, Global Investment Banks, Retail Banks, Capital Management Firms and many more have always worked in a data-dominated culture. They have always operated within the regulatory environment, where (paper-bound) transaction data is stored into massive silos.

The information-influx has further aggravated with the increase of electronic trading, loans, mortgages etc., as banks generate millions of monetary-related messages, each day. This eventually has made intelligent data entry and data processing — a prime need for the banking and finance sector.

Does this also mean that banks are equipped with latest and real-time information, which gives them ample opportunities of growth? No, the scenario is completely different. Data-influx has also created some pain-areas which eventually affect a bank’s overall operational efficiency.

A majority of banks, though equipped with latest technology, constantly struggle with aged or redundant data. This situation, basically results into major discrepancies and data inconsistencies.

Challenges of unaligned and visually chaotic mortgage loan records

Volume and velocity of soaring data leads these banks and financial institutions to a lot of unstructured, random and incoherent data. Deriving any insights from such unstructured set of information gives a tough time to banking professionals including Audit Managers, Business Banking Loan Administration Managers, Underwriters, Risk Management Directors, and Data Quality Analyst and so on so forth. It not only consumes their valuable productive hours, but even causes disruptions in their core operational areas.

Usually, critical documents like loan and mortgage forms are scanned to convert the paper-bound details into digital information. For example, the database which tracks mortgage loans will take ‘the name of the borrower’ from the original loan (paper-bound) application. And it might happen that the source document might have missing data or have an incomplete field.

When the data is not visually-classified, there chances of inconsistent information slipping out of sight. This results into much chaos. It might happen that a loan waiver is issued for a defaulter as you misinterpreted the incoherent information. Quite obviously, such situation results into loss to the bank. Moreover, stakeholder and other interested third parties like shareholders or regulators, generally, rely on the database values as the accurate substitute of the values presented in the supporting documents.

Structured data enables the stakeholder to detect variances, which in turn allow the banks to create safe-guard measures against any deliberate or inadvertent erroneous declaration of asset values, for risks mitigation.

How visually-classified data helps the banker?

When all the documents included in a series of transaction are visually-classified with proper labels, it is possible to list the data elements which occur frequently and to map the frequently occurring information to a database table and fields which can be used to store them.

Once the connections between the unstructured and structured content is mapped out, it can be further used for two main purposes:

  1. Data Input: Instead of constantly re-typing the same information, data elements can be efficiently pulled off from particular documents and can be placed in the database. This speeds up data processing and even enables automatic verification of information.
  2. Structured Content Data Values should be Audited/Validated: When structured digital data is entered into a tracking system, validating and verifying data values becomes much easier. Well-structured data makes it easy to check the entered data elements against the supporting documents. Moreover, signatures (of the same person) captured from different documents can also be easily compared and detecting any fraud becomes faster.

Conclusion

Tracking down discrepancies or inconsistencies in incoherent and completely unstructured data fails to give required insights. Eventually, resulting into frauds and further draining the bank of its valuable revenues.

Image source: linkedin.com

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