Alternative Data can Reshape Lending Processes

In the realm of technology and data-driven decision-making, the evolution we’re witnessing today reminds us of how science fiction movies once captivated our imaginations with their futuristic visions.

In today’s digital era, our perception of technology has shifted dramatically. Traditional credit scoring models, which were once the gold standard for assessing the creditworthiness of SMEs, have found themselves at a crossroads. 

As the lending landscape undergoes a profound transformation, much like the dazzling worlds depicted in science fiction, banks and financial institutions are now turning to alternative data credit scoring engines. These cutting-edge tools harness the power of non-traditional data sources, ushering in an era where lenders can attain a more comprehensive and accurate understanding of a borrower’s creditworthiness.


What is Alternative Data Credit Scoring?

Alternative data credit scoring refers to the use of non-traditional data sources, such as Social Media, Psychometric Profiling, Digital Footprint, Transaction Behavior, to assess SMEs’ creditworthiness. While traditional credit scoring models rely heavily on credit history and financial records, alternative data credit scoring engines broaden the scope by incorporating additional data points. 

By analyzing these alternative data sources, lenders can gain valuable insights into a borrower’s financial behavior and repayment capacity, especially for SMEs with limited or no credit history.


Unlocking the Power of Data

Data has become a valuable asset in the digital age, and its potential in reshaping lending processes is immense. With the advent of technology and the availability of vast amounts of data, lenders can now tap into a wealth of information to make more informed credit decisions. Alternative data credit scoring engines utilize advanced algorithms and machine learning techniques to analyze large datasets quickly and accurately. 

This enables lenders to identify patterns, trends, and correlations that traditional credit scoring models might overlook. By unlocking the power of data, banks can make better lending decisions, reduce default rates, and expand access to credit for underserved companies.


Implementing alternative data credit scoring engines can help banks mitigate risks and maximize profits.


By incorporating alternative data sources, lenders can gain a more holistic view of a borrower’s financial behavior and repayment capacity. This not only helps minimize risk but also allows for more accurate credit decisions, ultimately optimizing lending portfolios and increasing profitability for banks. 

With, iFactor Pure Analytics, we leverage alternative data sources to enhance efficiency, reduce fraud and default risks, and automate the process from sales to risk assessment to loan approval and monitoring on one single platform eliminating the intensive manual work, disordered communication and old style documentation, speeding up the process up to 80%.

In just minutes, the engine will deploy to the Bank’s user all and any data about a customer business morphology as well as its portfolio of debtors and suppliers. This data is further used by the sales, product and the underwriting team to pre-approve or approve the customer’s request in a faster and better informed way.

In conclusion, the power of data in reshaping lending processes cannot be underestimated, as it enables banks to unlock valuable insights, mitigate risks, and maximize profitability, following the philosophy to replace human research with real-time decision support, reducing time and errors, while allowing for due diligence on flag bases and sources.


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