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Discover our Customer Story featuring one of the largest banks in Italy. This Use Case highlights the importance of Alternative Data for financial institutions and linking the online reputation of small-medium businesses with their creditworthiness. By enhancing underwriting criteria with qualitative data, lenders have made the credit scoring process more accurate, objective and efficient.


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Alternative Data and Commercial Lending: The Future of Banking is on the Horizon

Over the last few years, we’ve witnessed the emergence of new, digital banks, each with the promise of a simple and fast customer experience. Anything from opening an account to transferring money requires just a few clicks.

Yet, the timeline for a commercial loan seems to have escaped this innovative concept. Small and medium businesses requesting financing still face a long and complex bureaucratic process, which is not sustainable for either party involved.

For this reason, many international financial institutions are adopting a ‘Smart Lending’ system based on Alternative Data. In essence, the commercial credit underwriting process is enriched with qualitative datasets for accurate, real-time and simple risk assessment. It’s 100% secure for both businesses and banks.

One of the most essential characteristics of Smart Lending is to refine and make risk assessment algorithms faster and more effective. Thanks to Alternative Data, or non-traditional datasets banks have on hand internally, lending institutions can take their evaluation one step further, by analyzing the reputation of both the business requesting the loan and the products and/or services they sell.

Among the most valuable Alternative Data for banks is qualitative sentiment data, data that relates to the reputation, degree of appreciation, satisfaction and perception of any business or brand in the eyes of customers.

This data provides banks with an even more complete, detailed and reliable economic picture of SMEs, therefore making it easier to evaluate its lendability and credit worthiness.




Prestigious Italian bank leverages online reputation as a financial assessment tool

In 2020, one of the major banks in Italy, and a customer of The Data Appeal Company, launched the integration of Sentiment data (of SMEs) into their credit underwriting algorithms.

The Goal

The bank’s goal was to collect, analyze and leverage Sentiment data to:

    • Enrich their proprietary B2B database and create a more complete profile of each business customer, including details on customer satisfaction, territorial location, contact information and services provided.
    • Incorporate sentiment data into their creditworthiness and underwriting algorithm to make the evaluation process more reliable, accurate and fast


    The Solution

    The bank provided The Data Appeal Company with an initial list of 98,000 SME customers to analyze. For each commercial client, The Data Appeal Company has mapped and collected current and historical data on the business’s:

        • Sentiment
        • Popularity
        • Business information (geo-coordinates, contact information, services provided, hours of operation, etc.)

      To make the analysis more reliable and complete, The Data Appeal Company has also developed a proprietary VAT Matching algorithm, which allows users to verify and validate the correspondence between brands and the VAT number of the individual businesses under analysis.
      The data, provided in API format, was seamlessly integrated into the banking institution’s internal system and made available to all of the bank’s branches.

      The Results

      The applications of Alternative Data and new technologies based on Artificial Intelligence offered by The Data Appeal Company have proven invaluable and versatile, going beyond the bank’s initial forecasts. This project has not only exceeded objectives, but has also supported the bank to improve the effectiveness of customer acquisition and commercial credit underwriting. 


      The bank has enhanced their commercial credit underwriting and risk management system, integrating reputational and Sentiment data into their algorithms. This has allowed for a more complete and accurate understanding of a business’s value, economic performance and competitive positioning.


      The greater reliability of the bank’s algorithms has reduced the risks associated with the insolvency of loans and investments.


      Thanks to the new information obtained, the bank has enriched the profiles of old and new customers with in-depth and real-time qualitative and quantitative data.


      The Sentiment and popularity datasets linked to both companies and territories has allowed the sales staff to target the most relevant companies to offer targeted banking products as well as identify areas and companies with the highest investment potential.


      The data and technology at The Data Appeal Company support the real-time analysis of any business around the world. In Italy alone, there are around 6,500,000 unique VAT numbers. This offers ample room for growth and accuracy for the bank’s smart lending process.

      Download the use case

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