{"id":1516,"date":"2019-07-22T16:35:58","date_gmt":"2019-07-22T13:35:58","guid":{"rendered":"http:\/\/beeeye.com\/banks-without-ai-based-credit-risk-modeling-struggle-to-keep-pace-copy-copy\/"},"modified":"2021-04-11T00:04:59","modified_gmt":"2021-04-11T00:04:59","slug":"why-2019-is-the-year-to-move-to-al-based-credit-scoring-modeling","status":"publish","type":"post","link":"http:\/\/beeeye.com\/why-2019-is-the-year-to-move-to-al-based-credit-scoring-modeling\/","title":{"rendered":"Why 2019 is the Year to Move to AI-Based Credit Scoring Modeling"},"content":{"rendered":"

As we awaken to 2019, recent financial technology developments are signaling a new disruptive era in credit scoring. Modern credit processes and risk modeling platforms are leveling up with algorithm-based solutions that are using Artificial Intelligence (AI) based innovations. Improved credit scores usage combined with data from alternative sources are offering deeper data insights, greater accuracy, and more financial transparency. In effect Machine Learning for credit risk models are closing an information gap between banks and consumers and opening opportunities for new consumer experiences.
\nThis blog explores why in 2019 it is imperative to deploy disruptive AI based credit scoring technologies, and how they can be leveraged to forge a new path to borrowers, while increasing the profitability of the lending business.<\/p>\n

Traditional credit scoring and the big lending problem<\/strong><\/h3>\n

Existing, yet outdated credit processes have often isolated many customers that are underbanked, underserved, and ignored. Driven by rigid score cut-offs, banks were renowned for rejecting potential consumers, even if they were only one point below. Still under the influence of the credit crisis, banks often focused on the debt-to-income ratio based on salaries vs. total monthly payments, to generate profiles, and justify rejections for payments above 50%. Even when a consumer\u2019s scores recovered, banks often continued to avoid their portfolios.
\nAt the end of the day, banks have spent much of the past 10 years chasing premium borrowers, yet that slice of the market is largely tapped out. To their detriment, they did not factor in how consumers managed their checking, savings, and money market accounts, and by doing so, isolated millennials, immigrant entrepreneurs and other consumer groups that showed high potential, yet did not qualify for the traditional credit cut-off.<\/p>\n

FICO is making changes, but are they in the right direction?<\/strong><\/h3>\n

FICO this year is embarking on a transformational change; rather than adhering to rigid cut-offs and traditional methodologies, the new \u2018UltraFICO Score\u2019 <\/a>plans to expand credit approvals by accounting for a wider profile transactions history. According to FICO \u2018many consumers are still locked out of mainstream credit, including 79M Americans with sub-prime scores (680 or below) and 53M Americans with not enough data on record for generating a FICO score\u2019. The new score is FICO\u2019s latest answer to lenders who, following years of mostly cautious lending, are seeking ways to boost loan approvals. FICO claims, some seven million applicants with low credit scores resulting from their credit history, would likely see their scores improve under the new system. In today’s fiercely competitive financial sector, such numbers are simply impossible to ignore.<\/p>\n

The question is, should score boosting merely entail widening the range of applicants, or is there a better way? Will it actually improve the credit risk modeling process, or will it increase overall risk?
\nPerhaps there is a more predictive, more advanced technology that can actually increase the pool of credit-worthy customers without taking a deep dive into risk?<\/p>\n

New data sources are lifting the Gini Coefficient<\/strong><\/h3>\n

In credit scoring, the two leading questions; how risky is a customer, and should an organization lend to a borrower given the borrower\u2019s risk, are still relevant. Yet, they need to be revisited following recent Machine Learning technology developments. More and more Innovation and Risk Managers are increasingly exploring the vantage points of Machine Learning based solutions in credit scoring modeling.
\nOne of the more popular ways to measure the performance of the PD model is the Gini Coefficient. Using advanced AI technology, by leveraging Machine learning for credit models and improving the Gini Coefficient, lenders can transform the way data is used to optimize predictive models and improve accuracy. For scoring purposes, AI is particularly effective at using previously underutilized data, as well as internal data and legitimate third-party data.<\/p>\n

Advanced ML modeling offering new opportunities<\/strong><\/h3>\n

In the world of credit scoring, Machine Learning modeling platforms are a powerful tool for gaining insights, within a relatively short time, into the potential opportunities in a market, or into the potential of new data sources previously overlooked. Based on re-engineered financial data from banks, and new trusted data captured online, Machine Learning technology can be used to leverage existing internal modeling processes used by lenders to perform smarter underwriting.
\nBut with all the great power of data, comes great responsibility, which is why credit score modeling technology developers need to steer away from the black box problem, that has characterized AI to date. As a result, bankers are seeking a new way of implementing AI technology in credit scoring, using a more transparent solution that is not influenced by biased figures and information. Opening up the \u2018black box\u2019 is an important part of understanding how the models are driven and the outcome of their use. This is why advanced credit scoring technology solutions that are embracing transparency, not only have a better chance of meeting regulations, but are also vital for avoiding error.<\/p>\n

Cost of forgoing the opportunity and missing the boat<\/strong><\/h3>\n

Inevitably, more banks, credit card companies and lenders will adopt machine learning technology to perform smarter credit score modeling in shorter time spans to a wider consumer market.\/p>
\nThe introduction of a Machine Learning layer of technology not only can help lenders reach a new stream of potential consumers, it holds the promise of increasing lenders profitability. Nevertheless, despite the growing interest, there is still the explainability issue that has created a major roadblock to full-scale adoption of AI credit scoring technologies.
\nIt has taken a new generation of AI-based credit modeling software to overcome the technology gap and open up the black box: <\/strong>Recent advances in AI credit scoring technology, are finally overcoming major transparency and stability pitfalls, in a bid to deliver a more robust solution that is ready for full-scale adoption.
\nThis was the missing part in the first generation of AI solutions for credit scoring.
\nApplying this next-generation AI technology can lead to more business for consumer lenders without the need to increase risk.
\nHow can advanced credit scoring technology benefit you?
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Find out more about our advanced AI-based solution for better credit scoring<\/strong><\/h2>\n

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