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Investing inherently involves a certain level of risk.

However, in today’s digital era, data presents the potential to significantly reduce, if not eliminate, this risk by mitigating the uncertainties associated with corporate and business financing investments and valuations.

And, one powerful way to do this is through data enrichment.

Data enrichment is the process of acquiring and integrating new datasets to back customer profiling and credit scoring with context-specific and qualitative data.

By gaining a deeper understanding of any companies you work with, you can significantly mitigate risks in assessing both their present and, perhaps more importantly, future performance.

Financial institutions such as banks and insurance companies can get a more comprehensive and precise insight into a company’s financial risk, allowing them to make well-informed and confident investment choices.

In this article we’ll go through four key aspects of the data enrichment process for credit scoring that will support you as you venture into this innovative yet complex field.


What is data enrichment, and how can banks enhance their credit scoring algorithms with additional data?

Banks and financial institutions handle vast amounts of data on customers, companies, transactions, and markets.

However, much of this information may be poorly structured, and at times, incomplete. And without the appropriate information, it’s easier to make misguided financial decisions.

Data enrichment is the missing link between raw data and valuable information, one that allows banks to gain a deeper insight into investment opportunities, and any potential associated risks.

There are four primary types of data available to any bank or company, classified based on their respective sources.

Types of data by origin

Customer-provided data (zero-party)
The term zero-party data coined by Forrester, refers to data shared directly by the consumer with a brand or company, usually through surveys, quizzes, forms or other interactions.

First-party data
First-party data is data obtained by embedding tracking pixels in websites, social profiles, or applications. It is anonymous information about user demographics and behaviour.

Second-party data
Usually acquired through a trusted partner, second-party data is information shared between two parties that includes their own customer data, allowing for insights and more effective profiling of specific audiences.

Third-party data
Third-party data are collected from external sources not directly related to the company by companies specialising in data collection and sales. They are often used to supplement existing data and provide insights into companies and consumers.

Usually, the process of data enrichment involves integrating second or third-party data, which banks cannot directly access but hold significant value for a more dependable and up-to-date credit scoring model, into the bank’s proprietary business intelligence systems and creditworthiness algorithms.

Sentiment and popularity data as indicators of company profitability

As we have seen, third-party data is most often of interest to banks and insurance companies.

Banks typically have comprehensive access to objective company history information like balance sheets, cash flows, and past performance.

What is often lacking to create a 360-degree overview is the subjective information related to companies and their operations.

This is where ‘alternative data’ comes into play. Although this kind of data differs from traditional financial data that banks typically possess, it’s still equally important.

Company performance is inherently linked to the quality of its work and the trust it builds with its end customers. So, when considering the performance of any company, focusing on these end customers is of utmost importance.

This includes considering aspects such as public perception, sentiment, and popularity, all of which play a vital role in shaping the relationship between the company and its customers.

Furthermore, the stronger and more positive these aspects are, the greater the likelihood that the company will experience robust and enduring financial performance.

A well-regarded company with a positive image is more likely to attract customers and investors and generate positive word-of-mouth, increasing its opportunities for growth and financial success.

In other words, you can consider sentiment and popularity as proxies for the profitability of the company and its Net Promoter Score (NPS).

This metric is a crucial indicator that any bank should consider when evaluating investments and financing, as it measures customer satisfaction and the likelihood that customers will recommend the company to others.

A high NPS is often associated with higher customer loyalty and better financial performance. Research conducted by CustomerGauge found that an increase of more than 10 points in NPS was associated with a 3.2% increase in revenue.

Already in 2005, the London School of Economics also calculated that a 7% boost in the NPS score is equivalent to a 1% increase in overall revenue. Similarly, Bain & Company states that the NPS benchmark gives a substantial competitive edge, attributing 20% to 60% of the fluctuation in organic growth rates among competitors to this metric.

Moreover, it’s important to note that the metrics reflecting the ‘qualitative performance’ of Italian SMEs, including their reputation, popularity, and customer perception based on direct experiences, have already shown significant predictive effectiveness as Early Warning Indicators.

In fact, data enrichment based on such qualitative data, together with more traditional data give a complete picture of business performance.

credit scoring - esg

Data enrichment and ESG factors

ESG risks stem from environmental, social, and corporate governance factors (ESG), and are expected to play a crucial role in determining Risk Appetite Framework (RAF) — which defines acceptable risk levels aligning with organisational objectives. 

Again, the only way banks and insurance companies can include ESG factors in their analysis algorithms is through data enrichment.

In this case, banks need the right tools and data sources for trustworthy, high-quality information, while having the know-how to interpret and integrate the data into the bank’s analysis systems.

Integrating ESG data into financial assessments helps identify and reward companies with a long-term and sustainable vision. This is particularly vital as new European regulations require companies to demonstrate tangible sustainability efforts and show ongoing commitment.

Once again, ESG factors are intricately tied to a company’s turnover.

“Environmental, social, and governance issues are indeed more than just an ethical concern: they clearly impact profit, as we can demonstrate.
Fully automated company geolocation, vulnerability of storage or production sites, materiality mapping, sentiment analysis, increased costs due to carbon taxes, litigation exposure, reviews, and supply chain correlations — all are tangible examples of alternative data, that, if properly integrated and weighed in an assessment approach, enhance the analysis, providing a comprehensive risk and opportunity evaluation in the ESG sphere.”

Valentino Pediroda, CEO of modefinance

Sustainability and sentiment for the finance industry: Data Appeal’s tailor-made solutions

The Data Appeal Company provides several financial intermediaries with its proprietary Sentiment and Popularity indexes, already used to proactively assess SME credit risk and to monitor corporate performance.

The Sentiment Score is calculated and updated in real-time based on reviews and web content from over 130 data sources.

The Popularity Index, on the other hand, considers the volume of digital content related to the physical locations where the company operates, and can also be analysed by time slot.

By combining the company’s historical data with more current data, banks can get a complete picture of the current situation and build a more robust predictive model.

Among our customers is one of Italy’s largest banks.

The bank’s objective is to leverage online sentiment data to do B2B data enrichment, adding more details on customer satisfaction, location, contacts, and services offered to the profile of each company.

In addition, the bank has incorporated sentiment data into its creditworthiness algorithm to make it more reliable and faster in assessing applications.

98,000 SMEs were analysed and mapped, enriched with quantitative data, sentiment scores and popularity data. The data, provided in API format, was integrated into the bank’s systems via a system integrator and made available to all territorial branches.

In addition, the bank refined its credit scoring system by integrating the reputation data of each company into the algorithms. This has provided a more complete and reliable picture of a company’s value, economic performance, and positioning.



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