Validating Risk Models With Customer Data

I was tired of guessing, and being wrong. Here's how I'm using AI to build customer health scores. As someone who's used Customer Success software for over 10 years and works with companies to design their health scores, I can tell you, this has always been a challenge. Most folks were working off assumptions, copying what others had done, or over-engineering scores thinking more inputs meant more accuracy. We’ve all seen it: ✅ Green customers churn ❌ Red customers renew And every time, we scratch our heads and ask ourselves, what are we getting wrong? This doesn't make sense. AI can give us the answer. It allows us to look at everything ... who our customers are, how they behave, what they need, and what they actually do. And from that, we can build truly intelligent profiles of health. No more guessing. Here’s a 5-step process that I used to redefine health: 1️⃣ Redefine your segments Move beyond spend-based segmentation. Segment by journey stage, product use case, or engagement pattern to get more meaningful insights. 2️⃣ Enrich your data Pull together all available data, product usage, support interactions, sentiment signals, firmographics, and demographics. The richer the picture, the better the model. 3️⃣ Label your historical outcomes Identify which customers renewed, expanded, or churned over the past 12–24 months. These become your training labels. 4️⃣ Run AI modeling Use AI to analyze patterns across your segments and outcomes. Prompt it to define health indicators tied to success and risk. 5️⃣ Operationalize in real time Build the model into your workflow. Let it learn and adapt as new data comes in so your health score always reflects what’s actually happening, not what you assumed. The goal isn’t to be perfect. The goal is to be accurate enough to act with confidence. Bonus: Loop in your CS teams to validate and pressure test the output. They’ll help refine the model and drive adoption. What’s powering your health score today ... insights or assumptions?

Credit Risk Modeling: Model Risk And What To Look Out For. When it comes to validating credit risk models, there are some who view validation as nothing more than replication work and statistical testing. There is a lot more to reviewing credit risk models than pure replication and p-values. A model reviewer should be able to tell if the model is going in the right direction by looking at a few things, before even going into details, down to the data. 1. The data profile, how much history, how many variables were considered etc. 2. The default definition, or bad definition. How bads are defined, what is considered "performing" and anything in between (indeterminate cases). 3. The segments of the model. Like probability of default PD model, may be segmented by time on book (months on book MOB) i.e new to bank (MOB < 6 months etc), existing to bank, new to bureau etc. Segments may make use of application scores A-scores, behaviour scores B-scores. What analysis is performed to get appropriate segments. 4. Model discrimination testing. - Discrimination here refers to the model output prediction's ability to distinguish the bad borrowers from performing borrowers. - For PD usually, measured by the Gini coefficient, or so-called Accuracy Ratio AR. - High or relatively low AR, what is acceptable. The intuition. 5. Model calibration. - Models must be calibrated. - For PD models, review how the central tendency CT is determined. How is the long run PD set? What were the downturn years. Is there a mix of high and low ODRs, and any margin of conservatism. - For LGD, EAD, similarly, what are the downturn years. Are actual losses truly peaking in those years. - Is segment level calibration necessary? Like segment level CT? Why or why not. 6. Calibration test (or test for conservatism) - PD, for newly developed models, the average PD shouldn't be too close to the ODR. Or even breaching it. It indicates under-calibration or something amiss with cyclicality. It doesn't look good to regulators when seeking approval. - Similarly for LGD / EAD, at the time of seeking approval, shouldn't have under-calibration 7. Last but not least. The credit intuition behind each model. Each variable. Each segment. Some credit sense. These few areas, a model reviewer should be able to tell much about the model. And provide sufficient effective challenge if required. During a full fledged review, of course then replication comes in, to inspect the granular data. Inspecting all the various steps like Single Factor Analysis SFA, Multi Factor Analysis MFA etc. However before even going there, if the data profile, definition of bads, or segments, or CT looks amiss or lacks justification, or if the calibration is inadequate, it should be highlighted. Not necessarily a total failure of the model but any breaking points, risks to be addressed. Of course, we have the all powerful Margin of Conservatism (MoC). Just slap on some.

The latest developments in #PD (Probability of Default) model validation focus on ensuring models are reliable and compliant through techniques like #backtesting, #benchmarking, and stress testing to assess performance across different economic conditions. A crucial aspect is establishing an independent validation function to provide unbiased reviews and maintaining a comprehensive model inventory. Validation methods now also include the assessment of data quality, theoretical validity, and the statistical validity of the model. 𝗞𝗲𝘆 𝗔𝘀𝗽𝗲𝗰𝘁𝘀 𝗼𝗳 𝗟𝗮𝘁𝗲𝘀𝘁 𝗣𝗗 𝗠𝗼𝗱𝗲𝗹 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: 𝘐𝘯𝘥𝘦𝘱𝘦𝘯𝘥𝘦𝘯𝘵 𝘝𝘢𝘭𝘪𝘥𝘢𝘵𝘪𝘰𝘯: Establishing an independent function to assess models without bias from development or business units is a key requirement.  𝘊𝘰𝘮𝘱𝘳𝘦𝘩𝘦𝘯𝘴𝘪𝘷𝘦 𝘈𝘴𝘴𝘦𝘴𝘴𝘮𝘦𝘯𝘵: Validation involves a deep dive into the model's design and performance, including: 𝘋𝘢𝘵𝘢 𝘝𝘢𝘭𝘪𝘥𝘪𝘵𝘺: Ensuring the quality and suitability of the data used for training and testing.  𝘛𝘩𝘦𝘰𝘳𝘦𝘵𝘪𝘤𝘢𝘭 𝘝𝘢𝘭𝘪𝘥𝘪𝘵𝘺: Confirming the underlying logic and theoretical underpinnings of the model are sound.  𝘚𝘵𝘢𝘵𝘪𝘴𝘵𝘪𝘤𝘢𝘭 𝘝𝘢𝘭𝘪𝘥𝘪𝘵𝘺: Evaluating the model's statistical soundness and performance through various quantitative tests.  𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀: 𝘉𝘢𝘤𝘬𝘵𝘦𝘴𝘵𝘪𝘯𝘨: Periodically testing the model against historical data to see how well it predicted actual outcomes.  𝘉𝘦𝘯𝘤𝘩𝘮𝘢𝘳𝘬𝘪𝘯𝘨: Comparing the model's performance against alternative or industry-standard models to gauge its relative effectiveness.  𝘚𝘵𝘳𝘦𝘴𝘴 𝘛𝘦𝘴𝘵𝘪𝘯𝘨 𝘢𝘯𝘥 𝘚𝘤𝘦𝘯𝘢𝘳𝘪𝘰 𝘈𝘯𝘢𝘭𝘺𝘴𝘪𝘴: Assessing the model's resilience and performance under severe but plausible future economic conditions or stress scenarios.  𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗲𝗱 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆: Maintaining a detailed inventory of all models, including their documentation, performance results, and code, is essential for ongoing monitoring and validation.  𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗼𝗳 𝗠𝗼𝗱𝗲𝗹𝘀: Validation practices are adapting to different types of PD models, such as those that are point-in-time (PIT) or through-the-cycle (TTC), which respond differently to economic shifts.  Noting that EBA and HKMA as well as other regulatory authorities have recently published final guidelines pertaining to credit risk internal model validation frameworks, the attached compilation addresses the latest research on the topic covering novel techniques and approaches. #riskmanagement #creditrisk #defaultrisk #riskmeasurement #riskmitgation #pointintime #ECL #throughthecycle #TTC #modelvalidation #riskmodel #Expectedcreditloss #probabilityofdefault #riskassessment #internalmodel #IRB #Basel #stresstesting #capitaladequacy #solvency #information #resources #knowledge #research #modelgovernance #MRM #modelrisk #dataquality #validation

🚨 New Resource: FairPlay’s Model Validation Field Guide 🚨 Model validation is no one’s favorite task—but it’s absolutely essential. Especially now. Regulators are taking a hard look at models using AI and alternative data. Courts are questioning their legal defensibility. And inside many financial institutions, data scientists, compliance officers, and legal teams are still struggling to speak the same language. That’s why we created FairPlay’s Model Validation Field Guide. This free, practical handbook is designed to help financial services and insurance companies validate their high-risk models—faster, smarter, and with more confidence. Inside, you’ll find: ✅ Step-by-step checklists for every phase of validation ✅ Plain-English guidance on conceptual soundness, data quality, process integrity, outcomes testing, monitoring, and governance ✅ Questions every model reviewer (technical or not) should be asking ✅ Tips for aligning your validation efforts with FDIC and OCC guidance Whether you're validating a credit score, pricing model, fraud detection system, or AI/ML underwriting tool, this guide will help you build a defensible, transparent, and efficient review process. 📘 Download the Field Guide here: https://lnkd.in/gawmevye And if you need independent model validation support—or just want to make sure your next review stands up to regulatory scrutiny—call FairPlay. We’d be happy to help!

Your model delivered a KS of 0.82 during validation. Six months later, approvals dropped, bad rates increased, and business teams lost trust. What changed? 📉 One of the biggest misconceptions in risk modeling is believing that model development is the hard part. In reality, deployment is where the real battle starts. A credit risk model can degrade due to: ✅ Population drift ✅ Economic shifts ✅ Underwriting policy changes ✅ Bureau behavior changes ✅ Data pipeline issues ✅ Reject inference bias And interestingly, your ROC-AUC may still look stable. Why? Because ranking power alone doesn’t guarantee business stability. For example: A model can still rank customers correctly 🔹But calibration may deteriorate 🔹Approval mix may change 🔹Portfolio quality may shift 🔹Risk thresholds may become outdated This is why monitoring only PSI is dangerous. Modern model monitoring should include: 🔹 Feature-level drift 🔹 Segment-wise stability 🔹 Calibration tracking 🔹 Approval-rate movement 🔹 Vintage analysis 🔹 Economic overlays In banking ML, the challenge is not building a good model. It’s keeping it reliable in a changing world. 🎯 Interview Questions: 1. Difference between concept drift and data drift? 2. How would you detect model degradation before delinquency outcomes mature? 3. Why can a stable AUC still hide business deterioration? Curious to hear from others working in risk analytics 👇 What’s the fastest model deterioration you’ve seen after deployment? #MachineLearning #CreditRisk #BankingAnalytics #RiskModeling #DataScience #ModelMonitoring #Fintech #AIInFinance #CreditScoring #MLOps #Analytics #ModelRiskManagement

Build a callback-worthy Credit Risk Modelling Project Most Credit Risk Scoring projects are assumed to be complete when it hits a high ROC-AUC. But in the real world, a model’s success isn’t defined by its best day but by how stable and reliable it remains over time. That’s exactly what makes Kaggle's Home Credit – Credit Risk Model Stability competition (found here - https://lnkd.in/gHvcCJ3t) a powerful learning experience. It’s not just about accuracy, but it’s about understanding if and why the prediction holds good even six months later. 🧩 Dataset Understanding The dataset is rich and realistic. It includes application data (customer demographics, income, and loan information), bureau records, previous applications, and detailed payment histories. Working with this data teaches you how to build relationships across multiple tables, handle missing values intelligently, and uncover behavioral patterns that drive credit risk. You can create dynamic features like credit utilization ratios, repayment delays, or trends in overdue payments over time. You can also experiment with aggregating features at the customer level - such as total credit exposure, number of open accounts, or recent delinquency patterns. ⚙️ Model Selection and Eval A good starting point is Logistic Regression, which is still a gold standard in credit risk modeling, before experimenting with more advanced algorithms like LightGBM, CatBoost etc. In this competition, performance isn’t just about accuracy. You’ll be judged on how stable your model is across different time periods. Metrics like AUC-ROC and the Kolmogorov–Smirnov (KS) statistic measure discrimination, while the Population Stability Index (PSI) and Model Stability Metric (MSM) capture how much your predictions drift over time. 🔍 Error Analysis and Responsible AI After your model is built, spend time understanding where and why it makes mistakes. Are you misclassifying younger borrowers more often? Are certain income brackets or occupations being unfairly penalized? These questions are critical for both model improvement and ethical AI. Techniques like SHAP or LIME can help explain individual predictions, allowing you to uncover potential biases and improve interpretability. 💼 Key Takeaway When presenting this project, go beyond showing metrics. Visualize your results i.e. plot feature importances, show PSI trends over time, and highlight how your model maintains stability across different months. 𝘚𝘩𝘢𝘳𝘦 𝘯𝘰𝘵 𝘫𝘶𝘴𝘵 𝘸𝘩𝘢𝘵 𝘺𝘰𝘶 𝘣𝘶𝘪𝘭𝘵, 𝘣𝘶𝘵 𝘩𝘰𝘸 𝘺𝘰𝘶 𝘳𝘦𝘢𝘴𝘰𝘯𝘦𝘥 𝘵𝘩𝘳𝘰𝘶𝘨𝘩 𝘦𝘢𝘤𝘩 𝘥𝘦𝘴𝘪𝘨𝘯 𝘤𝘩𝘰𝘪𝘤𝘦. --- 🚶➡️ To land your next Data Science role, follow me - Karun! ♻️ Share so others can learn, and you can build your LinkedIn presence! (Img Src: https://lnkd.in/g4yhJ_iV)

For twenty-five years I ensured software worked as users expected; for the last four I’ve focused on AI that learns and changes. Today AI often moves straight from training to production, and that speed hides real risk. Below are recent failures, their root causes, and why QA must be mandatory. Example 1 — Air craft company chatbot: The AI promised a bereavement refund that didn’t exist in the airline’s policy. The passenger claimed it, the airline refused, and a tribunal ordered Air craft to honor the chatbot’s promise, costing the company money. Root cause: The model hallucinated policy and was not validated against the official source before release. Example 2 — Hospital AI misclassification: An AI system used to prioritize patient care rated Black patients as lower risk than equally sick White patients. This led to delayed treatment for some patients and unequal access to resources. Root cause: The training data used historical billing costs as the label, which reflected past under-treatment and bias, not true health needs. Example 3 — Autonomous vehicle crash: A self-driving car failed to recognize a pedestrian in low light and collided, causing serious injury. Root cause: The perception model lacked sufficient night and low-contrast scenarios in training, and robustness testing against edge cases was incomplete. These failures show the same pattern: models trained on biased or incomplete data, insufficient scenario and adversarial testing, and no guardrails or oversight before production. Root causes include label bias, dataset gaps, hallucination without source validation, and weak monitoring for drift. Because AI changes behavior over time and can amplify bias or fabricate facts, QA is mandatory for every AI product: validate data and provenance, run scenario-based functional tests, perform adversarial and robustness assessments, monitor continuously for drift, audit privacy, measure explainability and fairness, and require test evidence before any model change goes live. SpearSoft will take care of your QA journey end to end: we design and execute the right tests, set up continuous monitoring, produce compliance-ready artifacts, and governance that ties model changes to test evidence. Build AI you can trust—testing is not optional, it is a duty to your users and your business.