Analysis Featured

AI Credit Scoring: Fad or the Future?

GiniMachine density distribution

It had to happen. Artificial intelligence is shaking up the fintech world.

The opportunity to get an in-depth analysis of client’s creditworthiness, have fast credit decisions, and score thin-file people makes AI a goldmine for many lenders today.

Besides, machine learning and AI-based techniques have become affordable (like never before) to a wider audience, empowering small and midsize lenders with the latest technology tools.

Why Today Lenders Ignore Traditional Scoring

For years, traditional scorecards, linear models, decision trees, and so widely-used FICO scores have played a significant part in a decision-making process. Although they don’t tell an applicant’s full history and can’t cope with big data, they are still used by the 90% of the top industry players.

However, more and more lending startups come to the conclusion that ‘your grandpa’s approach to data analysis’ is not enough. Because many traditional underwriting systems are missing out on a huge number of deserving borrowers.

To get traditional credit, you often need a credit history. However, according to ID Analytics, nearly 20% – or 45 million U.S. consumers – have no credit history or lack sufficient information to generate a credit bureau score. Sounds like a catch-22.

Is AI Making Credit Scores Better?

Ultimately, AI needs to improve the ‘underperforming loans’ metrics and optimize risks vs. returns for each loan issued. If it’s not doing this, then it fails.

The Efma report found that 58% of banking providers believe AI will (eventually) have a significant impact on the fintech industry.

Today, lenders have been using machine learning algorithms to solve problems both big and small, by making manual processes more simple, accurate, faster and less expensive.

“AI allows you to better or more accurately predict the one’s probability of default,” says Peter Maynard, SVP of enterprise analytics for Equifax. “Because each attribute can have multiple weights.”

Can AI Outperform Experts?

Today, in order to build a well-performing scoring model, you need to hire an expert with a fairly extensive and specific expertise in the field of mathematical statistics. Besides, this expert needs complex, specific, and very expensive software to build a model. In the end, you’ll spend hundreds of thousands of dollars and no one guarantees you results.

Paul Meehl, a clinical psychologist and professor of psychology at the University of Minnesota, conducted an experiment: He compared the predictive power of human experts with simple algorithms. The results showed that in all 20 cases, simple algorithms outperformed experts based on data such as past test scores and records of past processing.

However, algorithms won’t replace humans in complicated fields. Maybe someday, but not now. Today, AI is aimed to help experts. Scoring solution providers strive to ensure that machine learning techniques can be easily used by any lender without a need to hire a team of data scientists and developers.

AI Scoring as a ‘Black Box’

Due to the inability to understand how decisions are made, AI has been seen as a ‘black box’. Lenders admit that the hidden part of the underwriting process (in particular, how it makes predictions) looks unreliable and insecure. Business owners want to avoid the appearance of biased credit rejections, and quite often look for explainable and accountable solutions.

However, AI-based credit scoring is of limited interpretation because of its complexity. And this complexity is quantitative, not qualitative. Let me explain:

AI scoring is a set of simple rules (Age – 25+, gender – female, income is more than 1.000 but less than 1.200, etc.). These rules can be created by the tens of thousands, and it’s impossible to track all of them by with human skills.

GiniMachine, an AI-based Credit Scoring Solution

GiniMachine shares an IBM principle: Machines should do all the hard work, freeing people to think. The core idea of the solution lies in building predictive models of high quality at a reasonable cost.

Founded in 2016, a young but fast-growing fintech startup, GiniMachine aims to fight bad loans with AI.

GiniMachine combines lenders’ data insights with the best machine learning algorithms, supplemented with the set of heuristics and methodological findings. The software allows you to build custom scoring models in a few seconds without attracting expertise in the field of mathematical statistics and machine learning.


GiniMachine density distribution

Today, lenders’ decisions to issue a loan are influenced by the credit score as well as general data like socio-demographic information or place of work of the borrower. Today, lenders take into account data from mobile devices, social media networks, the time of filling out the questionnaire, etc.

“Collecting and processing all these parameters costs a lot of money. However, not all of this information is necessary when drawing up a successful model for assessing the creditworthiness of the borrower. One of the tasks of GiniMachine is to determine which of the parameters are really important,” says Ivan Kovalenko, Co-founder of GiniMachine. “An important feature of our platform is that it knows how to work with raw data. As a result, the model built for each client is unique.”

Bottom line:

AI promises to bring lenders worldwide to the technology breakthrough. Armed with powerful solutions and best machine learning approaches, financial organizations can more effectively respond to the increasingly demanding customer base and ultimately increase acceptance rates.


Natalie Pavlovskaya
Natalie Pavlovskaya

Natalie Pavlovskaya is CMO at GiniMachine, an AI-based solution for fighting bad loans. She has been actively involved with digital marketing since 2011 and has a broad range of expertise. She is passionate about fintech, AI, and e-commerce.


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