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    Different challenges, one solution: Credit risk management for banks and fintech

    A guest post from Dr. Martin Seeger, Credit Risk Manager and Data Scientist, BeeEye's Advisory Board member Credit risk modeling poses different challenges to banks and Fintech companies. A solution can lay in a modeling platform that caters for both In the context of credit risk modeling, traditional financial institutions (e.g., banks, insurers) and Fintech companies both have very specific risk profiles they need to maintain. For both, the cost of inaccuracy is incredibly high.  However, banks and fintech companies face two very distinct challenges when it comes to achieving this covetable accuracy. Banks’ barriers Updating your platform once in a blue moon If you’re a risk analyst or modeller in a traditional financial institution, you are likely all too familiar with regulations’ impact on internal processes. Specifically, how regulations stretch out these processes in a way that may hurt modeling accuracy. Usually banks perform rating model validations only once a year due to the complexity of the processes regulations require. When they do, validation data may be a year [...]

    By | May 31st, 2020|General|0 Comments

    Credit risk assessment and discrimination: Reducing sectoral biases in credit allocation

    As the financial implications of the COVID-19 crisis are starting to unfold, banks and other lenders are under increased scrutiny. The public, press and governments are blaming these financial institutions for taking advantage  of the increase in requests for credit. There are many things credit lenders can do to provide better service to hurting households and businesses. , from improving their digital experience as they’re sheltering in place, to offering different loan routes. In this post, we want to address one aspect of credit rsk management: The biases that can be inherent to AI-based credit risk modeling. Inherent biases in AI-base modeling There are two main reasons why AI-based models are more prone to inherent biases than simple models: The black box: In a simple model, like a scorecard, we know what the weight of each feature in the model is. In AI/ML based models, it’s much more difficult to see how much each feature affected the model. This of course brought on the demand to increase model explain-ability, which [...]

    By | May 19th, 2020|General|0 Comments

    Credit risk modeling platforms: One-size fits…nobody

    No one knows for sure exactly when the first “one size” clothing garment was manufactured. But it seems that despite the longevity of this fashion phenomenon, no one has ever made a one-size-fits-all garment that fits even most of its target customers. The situation is similar when it comes to SaaS, and specifically risk management modeling platforms. Here are five reasons why a solution that is tailored to your business, especially if you’re a bank or a credit lender, is the right choice.  The benefits of an industry-specific credit risk modeling platform Industry features When a tool is designed for a specific industry, it’s designed with specific users in mind. Its creators consider their users’ internal processes, KPIs, the common problems they encounter and the steps they take to resolve them. For example, an automatic feature generator to help you improve your model’s predictions will likely not be found in a generic machine learning based modeling platform. Another often overlooked issue with generic solutions is the complex integration to external [...]

    By | May 4th, 2020|General|0 Comments

    Responsible credit management in times of crisis: With great power comes great responsibility

    If there’s anything we’ve learned from the 2008 credit crisis, is that credit lines should be extended with the utmost care. It seems like a no-brainer: lenders are using credit modeling to do just that, right? But even the most well-intentioned, backed-by-advanced-technology models out there can lead to dire outcomes. Especially if they aren’t built on an agile-enough platform that matches an ever changing reality. Below are some ways the right modeling platform can impact your results. For the sake of both your customers and your bottom line: Take care of your customers When a customer is given credit, it’s always at the expense of someone whose request was denied. When your model predicts their default risk, you want it to be based on the latest financial and market data. If, like most lenders, your modeling platform's update cycles are a few months long at best, you’re doing your customers a disservice. Why? Because when the market conditions change but your model is being fed 6-months old data, some previously [...]

    By | April 27th, 2020|General|0 Comments
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