Case Overview

A fintech firm deployed a text analysis based solution to identify active loans and cluster expenses

The fintech startup client is looking to use text analytics based solution to solve one of their customer data processing issues. As a feature of the platform, they ask loan applicants to upload previous three months of bank statements and credit card statements. The operations team then manually looks at these statements for any running loans and the expense heads. The client needs an automated statement processing solution to extract running loan info and to segment expenses.

Technology driven efficiency

The client was looking for a text analysis based solution that could scale and reduce the time of manual processing of bank and credit card statements with a high degree of accuracy.

  • Reduced the turn around time (TAT) for document processing to 3 minutes
  • Applicants expenses report and product reco generated in less than 5 minutes
  • Data automatically pre-processed and mapped to customer data platform

The solution has reduced the processing time of documents, leading to speedier approval or rejection decisions. A list of recommended products also generated using expenses analysis.

Need for automated, reliable solution.

The fintech client is very open to data-driven solutions and creating efficient processes. Realising the manual effort required to process the documents, they understood that a technology driven solution is needed to scale up their operations and ensure fast loan decisions are being made.

The automated solution should also aid them in collecting data automatically from the statements, ensuring good and consistent data is being collected.

One of the challenges in creating the automated solution was the lack of standardization in bank statements. Identification of whether it is a bank account statement or a credit card statement was another challenge. A product recommendations algorithm was desired, but because the fintech was a new player in the market, they did not have enough data to create a product recommendation engine.

Improved data quality for data-driven solutions

The client commissioned the engagement with three objectives in place. The first objective was to process the statement files and extract information on loans and segment the customer expenses.

The second objective was to create a standardized, consistent data collection system using named entity recognition (NER) algorithms in text analytics. These datapoints would then be used as inputs to develop a scorecard for loan approval.

The final objective was to segment customers based on expense categories and amount. This information would then be mapped onto product attributes to recommend products based on item-user collaborative filtering algorithm. Developing the right ‘cold start’ strategy was the challenge owing to limited data on loan performance.