{ "source_file": "0930", "source_platform": "Medium", "author": "Benjamin Nweke", "title_listed": "What SHAP Can't Explain About Agentic AI Fraud", "keywords": "", "domain": "机器学习", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/what-shap-cant-explain-about-agentic-ai-fraud/", "publish_date": "2026-09-10", "corpus_stage": "", "notes": "", "id": "5c90fa717c1e", "collected_at": "2026-10-02T07:30:47", "title": "What SHAP Can't Explain About Agentic AI Fraud | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Agentic AI](https://towardsdatascience.com/category/artificial-intelligence/agentic-ai/)\n# What SHAP Can't Explain About Agentic AI Fraud\nWhy autonomous agents expose a new explainability problem in fraud detection\n[Benjamin Nweke](https://towardsdatascience.com/author/benjaminnweke/)\nSeptember 10, 20268 min read\n![Futuristic AI agent connected to a financial transaction network while a fraud detection interface analyzes a suspicious payment using feature-level explanations, illustrating the gap between explaining a fraud score and explaining an autonomous agent’s decision path.](../images/000.webp)\nImage by author (Generated with ChatGPT)\nA futuristic AI agent operates across a connected financial transaction system while a fraud detection interface analyzes suspicious activity using feature-level explanations. The illustration represents the explainability gap in agentic AI fraud, where models such as SHAP can explain why a transaction appears risky but not the agent decisions and actions that produced it.\nFraud stopped being a purely human activity sometime in the last year. Not all of it, obviously; I mean, most fraud is still someone typing on a phone somewhere trying to move money that isn't theirs. \nBut enough of it has shifted that [Experian's 2026 Future of Fraud Forecast](https://www.businesswire.com/news/home/20260113509639/en/Experian%E2%80%99s-New-Fraud-Forecast-Warns-Agentic-AI-Deepfake-Job-Candidates-and-Cyber-Break-Ins-Are-Top-Threats-for-2026) put a name on it. AI agents transacting and shopping on people's behalf are now a real driver of financial fraud. \nThe report calls it \"machine-to-machine mayhem,\" which sounds like a headline written to get clicks, and honestly, it worked. \nBecause a legitimate shopping agent and a fraudulent bot can show up in a transaction log looking basically identical. Nobody's figured out the tell yet.\nI want to sit with why that's a bigger deal than it sounds. Most fraud detection, at every scale, from a local fintech to a Fortune 500 bank, has quietly rested on the same assumption for a decade or more. \nWhich is that there's a human on the other end of the transaction, and humans leave fingerprints. \nThings like how they type, when they switch devices, basically the way someone behaves when they're anxious or in a hurry.\nEvery fraud model, whether anyone building it says so out loud, is trained to notice when a human stops acting like themselves.\nI ran into a smaller, dumber version of this exact problem while building a fraud detection system, one that leaned hard on SHAP to explain itself. \nIt's worth telling because the thing that got me out of trouble at the time is the exact thing I now think is running out of road.\nLearn this step by step with the interactive [AI Agents](https://roadmap.sh/ai-agents) roadmap.\n## The question that wasn't really about the model\nAt my presentation, one of the panelists leaned back in his chair and asked a question that had nothing to do with precision or recall.\n> If a CEO makes a one-million-dollar transaction, and a university student makes the same transaction, does your system flag them the same way?\nI remember the room going quiet, and I remember taking longer to answer than I wanted to. \nMy model, a Random Forest classifier I'd trained on the PaySim dataset after benchmarking it against XGBoost and LightGBM, didn't know or care who was transacting. \nIt looked at the transaction: amount, timing, device, velocity. \nBasically the same features for a CEO's account as for a broke undergrad's. \nOn paper, that's what fairness in ML is supposed to look like. In front of a panel whose entire job is finding holes in risk models, it looked like I'd missed something obvious.\nHere's what actually saved me, and it wasn't a smarter model. \nI'd wired SHAP into the pipeline sitting on top of the classifier, mostly because a supervisor earlier in the year had pushed back on treating the model as a black box, and it turned out to be the reason I survived that question. \nI could pull up the specific transaction, show the ranked SHAP values, and say: here's what actually drove this prediction, in order, and with numbers attached. \nThat question has stayed with me longer than almost anything else from that presentation. Not because of what it exposed about my model specifically, but because of the assumption sitting underneath the whole field: that the thing making a transaction has a life outside the transaction, a job, a history, a reason to be moving that particular amount of money. \nIn simpler words, context.\nIf you want the full build, the model comparisons, the PaySim dataset quirks, and what my supervisor actually said when he saw the SHAP output for the first time, I wrote it all up here: [I Trained Six Models for Fraud Detection, and the Best One Isn't in Production](https://towardsdatascience.com/i-trained-six-models-for-fraud-detection-and-the-best-one-isnt-in-production/). This piece picks up from that one but doesn't require you to have read it.\n[![](../images/body-693cabfec880aaafe9c8.jpg)I Trained Six Models for Fraud Detection, and the Best One Isn't in ProductionWhat a final-year project taught me about the gap between evaluation metrics and production decisionsBenjamin Nweke · 10 min read](https://towardsdatascience.com/i-trained-six-models-for-fraud-detection-and-the-best-one-isnt-in-production/)\n···\n## Why explainability stopped being optional\nBlack-box fraud models have always had a trust problem, agentic AI or not. \nThe models that perform best, deep ensembles, gradient-boosted trees, tend to be the hardest to interpret, and that's a genuinely annoying trade-off if you've ever had to explain a flagged transaction to someone who isn't a data scientist.\nIf a compliance analyst can't tell whether a flag is a real signal or just noise the model latched onto, they'll either rubber-stamp everything or ignore the model entirely. \nI've seen both happen, and neither one is why you built the thing.\nSHAP earned its spot in fraud stacks by answering a narrow, unglamorous question, not \"is this model good\" but \"why did it do this, for this one transaction, right now.\"\nIt's less exciting than model architecture, which is maybe why it doesn't come up in conversation as much as it probably should. \nA risk score with no reasoning attached is close to useless the moment a human has to act on it. That was my exact problem at the presentation. \nInstead of \"92% fraud probability,\" you get something like: the amount is high for this account, the account is under 48 hours old, or the merchant has prior flags.\nPeople trust that. \nOr at least they trust it more than a number with nothing under it.\n## The ground is shifting under the fingerprint\nHere's the part that bugs me. \nEvery explainability method built for fraud detection, SHAP included, explains a decision using features that describe a transaction: amount, time, device, velocity. \nNow stay with me here. \nThose features exist because they correlate with how humans behave when something's wrong. \nA student's account suddenly moving CEO-sized money looks strange precisely because we assume a stable human behind it, with a pattern of life the anomaly is breaking from.\nAn agent doesn't have a pattern of life in any way that maps onto those features. \nIt doesn't get tired at 2 am. It doesn't fat-finger a transfer or panic-send money to the wrong account. \nIt can transact at a speed and consistency no human could sustain for five minutes, and that breaks the whole premise fraud detection has quietly run on: that deviation from a human baseline is the signal. \nThis isn't a someday problem. It's already showing up in the numbers.\n![](../images/001.webp)\nImage by author. Data source: BioCatch, 2026 survey of 1,440 fraud management, financial crime, and risk professionals across 25 countries. \nThe number I find more interesting than the headline 84% is the 60% who expect AI-mediated banking to make traditional fraud defenses less effective. \nThat points to something deeper than criminals simply getting better tools. The signals those defenses were built around may themselves be changing.\nRegulators are already circling this, and not in the usual glacial way. \nNIST's [AI Agent Standards Initiative](https://labs.cloudsecurityalliance.org/research/csa-research-note-nist-ai-agent-standards-governance-gap-202), launched in February 2026, treats agents as identifiable non-human principals that need their own credential lifecycles and audit trails, on the reasoning that existing auth frameworks were built for humans and don't stretch to cover autonomous software.\nIn the US, it's already further along than a standards initiative. [Senator Mark Warner's draft AI AGENT Act](https://letsdatascience.com/news/senator-warner-proposes-ai-agent-registry-and-fiduciary-rule-4bee071c), floated in mid-2026, would create a federal registry of trusted agents and require them to act transparently in a user's interest on major platforms. \nI don't know if this exact bill survives contact with Congress; most don't in their first draft. \nBut the instinct behind it is the thing worth noticing: lawmakers have landed on identity and accountability as the load-bearing question, before the industry has agreed on how to even log an agent's reasoning in a way a human could audit later.\n## What breaks, and what doesn't\nI want to be clear: I don't think SHAP is dead or dying, trust me, not even close. \nFeature-level explanations still matter for the transaction-shaped slice of fraud, and humans aren't going anywhere as fraud actors, unfortunately. \nBut feature attribution on transaction fields only ever answered why did this look wrong. Agentic fraud is dragging a different question onto the table. \nWhy did this actor do this at all?\nThat second question wants a different kind of trace entirely. \nNot transaction features but the agent's actual decision path, what tools it called, what it was authorized to do, and whether the action it took falls inside or three steps outside the scope someone delegated to it. \nA system built to explain a Random Forest's verdict on a dollar amount has literally nothing to say about whether an agent went off-script upstream of the transaction it eventually made. \nThat's closer to auditing a trajectory than scoring a data point. \nI've gone looking for someone who's solved this properly and mostly found papers that gesture at the problem without actually closing it, which either means I'm bad at finding things or it's genuinely still open. Possibly both.\n(Also, and this is a side thought I can't fully back up: I wonder if part of the reason nobody's cracked this yet is that agent trajectories are logged by the same companies building the agents, and there's not a lot of incentive to make that logging legible to an outside auditor. That's more of a suspicion than an argument, so take it for what it's worth.)\nMy guess, and it's just a guess, is that this doesn't get fixed by making SHAP more sophisticated. \nIt probably gets fixed by borrowing from a completely different corner of AI safety, agent monitoring and trajectory evaluation, and then stitching it onto the explainability discipline fraud detection already has. \nOr maybe those two fields never really merge and someone builds a third thing from scratch. I go back and forth on which one is more likely.\n···\n## Final thoughts\nI built a system to catch a human doing something a human wouldn't normally do, and SHAP was the thing that made it defensible in front of a room of people paid to find its weak points. \nThat problem is still real, and still not fully solved, so this isn't me saying my fraud detection system or others like it are done for. \nBut the target has moved somewhere the current toolkit wasn't initially built to follow, and I don't think that's a small footnote. \nIt's the gap between explaining a score and explaining an actor. Right now most of us, me included, only really know how to do the first one.\nIf you're building or evaluating fraud detection, the question I'd actually sit with isn't whether your model is accurate. \nIt's whether your explanations still mean anything once the thing on the other end of the transaction stops being a person.\n···\n## Before you go!\nI write about the gap between what a model does and what you can actually prove it did, especially once the thing making decisions stops being obviously human. You can [subscribe to my newsletter](https://thatcsguy.substack.com/?r=79lldp&utm_campaign=pub-share-checklist) if you'd like more of that.\n**Connect With Me**\n * [LinkedIn](http://www.linkedin.com/in/nwekebenjamin)\n * [Substack](https://thatcsguy.substack.com/)\n\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Agentic AI](https://towardsdatascience.com/category/artificial-intelligence/agentic-ai/)\n# What SHAP Can't Explain About Agentic AI Fraud\nWhy autonomous agents expose a new explainability problem in fraud detection\n[Benjamin Nweke](https://towardsdatascience.com/author/benjaminnweke/)\nSeptember 10, 20268 min read\n![Futuristic AI agent connected to a financial transaction network while a fraud detection interface analyzes a suspicious payment using feature-level explanations, illustrating the gap between explaining a fraud score and explaining an autonomous agent’s decision path.](https://assets.insightmediagroup.io/media/1788814203456_bz3f84.webp)\nImage by author (Generated with ChatGPT)\nA futuristic AI agent operates across a connected financial transaction system while a fraud detection interface analyzes suspicious activity using feature-level explanations. The illustration represents the explainability gap in agentic AI fraud, where models such as SHAP can explain why a transaction appears risky but not the agent decisions and actions that produced it.\nFraud stopped being a purely human activity sometime in the last year. Not all of it, obviously; I mean, most fraud is still someone typing on a phone somewhere trying to move money that isn't theirs. \nBut enough of it has shifted that [Experian's 2026 Future of Fraud Forecast](https://www.businesswire.com/news/home/20260113509639/en/Experian%E2%80%99s-New-Fraud-Forecast-Warns-Agentic-AI-Deepfake-Job-Candidates-and-Cyber-Break-Ins-Are-Top-Threats-for-2026) put a name on it. AI agents transacting and shopping on people's behalf are now a real driver of financial fraud. \nThe report calls it \"machine-to-machine mayhem,\" which sounds like a headline written to get clicks, and honestly, it worked. \nBecause a legitimate shopping agent and a fraudulent bot can show up in a transaction log looking basically identical. Nobody's figured out the tell yet.\nI want to sit with why that's a bigger deal than it sounds. Most fraud detection, at every scale, from a local fintech to a Fortune 500 bank, has quietly rested on the same assumption for a decade or more. \nWhich is that there's a human on the other end of the transaction, and humans leave fingerprints. \nThings like how they type, when they switch devices, basically the way someone behaves when they're anxious or in a hurry.\nEvery fraud model, whether anyone building it says so out loud, is trained to notice when a human stops acting like themselves.\nI ran into a smaller, dumber version of this exact problem while building a fraud detection system, one that leaned hard on SHAP to explain itself. \nIt's worth telling because the thing that got me out of trouble at the time is the exact thing I now think is running out of road.\nLearn this step by step with the interactive [AI Agents](https://roadmap.sh/ai-agents) roadmap.\n## The question that wasn't really about the model\nAt my presentation, one of the panelists leaned back in his chair and asked a question that had nothing to do with precision or recall.\n> If a CEO makes a one-million-dollar transaction, and a university student makes the same transaction, does your system flag them the same way?\nI remember the room going quiet, and I remember taking longer to answer than I wanted to. \nMy model, a Random Forest classifier I'd trained on the PaySim dataset after benchmarking it against XGBoost and LightGBM, didn't know or care who was transacting. \nIt looked at the transaction: amount, timing, device, velocity. \nBasically the same features for a CEO's account as for a broke undergrad's. \nOn paper, that's what fairness in ML is supposed to look like. In front of a panel whose entire job is finding holes in risk models, it looked like I'd missed something obvious.\nHere's what actually saved me, and it wasn't a smarter model. \nI'd wired SHAP into the pipeline sitting on top of the classifier, mostly because a supervisor earlier in the year had pushed back on treating the model as a black box, and it turned out to be the reason I survived that question. \nI could pull up the specific transaction, show the ranked SHAP values, and say: here's what actually drove this prediction, in order, and with numbers attached. \nThat question has stayed with me longer than almost anything else from that presentation. Not because of what it exposed about my model specifically, but because of the assumption sitting underneath the whole field: that the thing making a transaction has a life outside the transaction, a job, a history, a reason to be moving that particular amount of money. \nIn simpler words, context.\nIf you want the full build, the model comparisons, the PaySim dataset quirks, and what my supervisor actually said when he saw the SHAP output for the first time, I wrote it all up here: [I Trained Six Models for Fraud Detection, and the Best One Isn't in Production](https://towardsdatascience.com/i-trained-six-models-for-fraud-detection-and-the-best-one-isnt-in-production/). This piece picks up from that one but doesn't require you to have read it.\n[![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/08/google-deepmind-lISkvdgfLEk-unsplash-scaled.jpg)I Trained Six Models for Fraud Detection, and the Best One Isn't in ProductionWhat a final-year project taught me about the gap between evaluation metrics and production decisionsBenjamin Nweke · 10 min read](https://towardsdatascience.com/i-trained-six-models-for-fraud-detection-and-the-best-one-isnt-in-production/)\n···\n## Why explainability stopped being optional\nBlack-box fraud models have always had a trust problem, agentic AI or not. \nThe models that perform best, deep ensembles, gradient-boosted trees, tend to be the hardest to interpret, and that's a genuinely annoying trade-off if you've ever had to explain a flagged transaction to someone who isn't a data scientist.\nIf a compliance analyst can't tell whether a flag is a real signal or just noise the model latched onto, they'll either rubber-stamp everything or ignore the model entirely. \nI've seen both happen, and neither one is why you built the thing.\nSHAP earned its spot in fraud stacks by answering a narrow, unglamorous question, not \"is this model good\" but \"why did it do this, for this one transaction, right now.\"\nIt's less exciting than model architecture, which is maybe why it doesn't come up in conversation as much as it probably should. \nA risk score with no reasoning attached is close to useless the moment a human has to act on it. That was my exact problem at the presentation. \nInstead of \"92% fraud probability,\" you get something like: the amount is high for this account, the account is under 48 hours old, or the merchant has prior flags.\nPeople trust that. \nOr at least they trust it more than a number with nothing under it.\n## The ground is shifting under the fingerprint\nHere's the part that bugs me. \nEvery explainability method built for fraud detection, SHAP included, explains a decision using features that describe a transaction: amount, time, device, velocity. \nNow stay with me here. \nThose features exist because they correlate with how humans behave when something's wrong. \nA student's account suddenly moving CEO-sized money looks strange precisely because we assume a stable human behind it, with a pattern of life the anomaly is breaking from.\nAn agent doesn't have a pattern of life in any way that maps onto those features. \nIt doesn't get tired at 2 am. It doesn't fat-finger a transfer or panic-send money to the wrong account. \nIt can transact at a speed and consistency no human could sustain for five minutes, and that breaks the whole premise fraud detection has quietly run on: that deviation from a human baseline is the signal. \nThis isn't a someday problem. It's already showing up in the numbers.\n![](https://assets.insightmediagroup.io/media/1788639270602_9kc7sc.webp)\nImage by author. Data source: BioCatch, 2026 survey of 1,440 fraud management, financial crime, and risk professionals across 25 countries. \nThe number I find more interesting than the headline 84% is the 60% who expect AI-mediated banking to make traditional fraud defenses less effective. \nThat points to something deeper than criminals simply getting better tools. The signals those defenses were built around may themselves be changing.\nRegulators are already circling this, and not in the usual glacial way. \nNIST's [AI Agent Standards Initiative](https://labs.cloudsecurityalliance.org/research/csa-research-note-nist-ai-agent-standards-governance-gap-202), launched in February 2026, treats agents as identifiable non-human principals that need their own credential lifecycles and audit trails, on the reasoning that existing auth frameworks were built for humans and don't stretch to cover autonomous software.\nIn the US, it's already further along than a standards initiative. [Senator Mark Warner's draft AI AGENT Act](https://letsdatascience.com/news/senator-warner-proposes-ai-agent-registry-and-fiduciary-rule-4bee071c), floated in mid-2026, would create a federal registry of trusted agents and require them to act transparently in a user's interest on major platforms. \nI don't know if this exact bill survives contact with Congress; most don't in their first draft. \nBut the instinct behind it is the thing worth noticing: lawmakers have landed on identity and accountability as the load-bearing question, before the industry has agreed on how to even log an agent's reasoning in a way a human could audit later.\n## What breaks, and what doesn't\nI want to be clear: I don't think SHAP is dead or dying, trust me, not even close. \nFeature-level explanations still matter for the transaction-shaped slice of fraud, and humans aren't going anywhere as fraud actors, unfortunately. \nBut feature attribution on transaction fields only ever answered why did this look wrong. Agentic fraud is dragging a different question onto the table. \nWhy did this actor do this at all?\nThat second question wants a different kind of trace entirely. \nNot transaction features but the agent's actual decision path, what tools it called, what it was authorized to do, and whether the action it took falls inside or three steps outside the scope someone delegated to it. \nA system built to explain a Random Forest's verdict on a dollar amount has literally nothing to say about whether an agent went off-script upstream of the transaction it eventually made. \nThat's closer to auditing a trajectory than scoring a data point. \nI've gone looking for someone who's solved this properly and mostly found papers that gesture at the problem without actually closing it, which either means I'm bad at finding things or it's genuinely still open. Possibly both.\n(Also, and this is a side thought I can't fully back up: I wonder if part of the reason nobody's cracked this yet is that agent trajectories are logged by the same companies building the agents, and there's not a lot of incentive to make that logging legible to an outside auditor. That's more of a suspicion than an argument, so take it for what it's worth.)\nMy guess, and it's just a guess, is that this doesn't get fixed by making SHAP more sophisticated. \nIt probably gets fixed by borrowing from a completely different corner of AI safety, agent monitoring and trajectory evaluation, and then stitching it onto the explainability discipline fraud detection already has. \nOr maybe those two fields never really merge and someone builds a third thing from scratch. I go back and forth on which one is more likely.\n···\n## Final thoughts\nI built a system to catch a human doing something a human wouldn't normally do, and SHAP was the thing that made it defensible in front of a room of people paid to find its weak points. \nThat problem is still real, and still not fully solved, so this isn't me saying my fraud detection system or others like it are done for. \nBut the target has moved somewhere the current toolkit wasn't initially built to follow, and I don't think that's a small footnote. \nIt's the gap between explaining a score and explaining an actor. Right now most of us, me included, only really know how to do the first one.\nIf you're building or evaluating fraud detection, the question I'd actually sit with isn't whether your model is accurate. \nIt's whether your explanations still mean anything once the thing on the other end of the transaction stops being a person.\n···\n## Before you go!\nI write about the gap between what a model does and what you can actually prove it did, especially once the thing making decisions stops being obviously human. You can [subscribe to my newsletter](https://thatcsguy.substack.com/?r=79lldp&utm_campaign=pub-share-checklist) if you'd like more of that.\n**Connect With Me**\n * [LinkedIn](http://www.linkedin.com/in/nwekebenjamin)\n * [Substack](https://thatcsguy.substack.com/)\n\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657271062_x78p7k.jpeg)\nWritten By\n## [Benjamin Nweke](https://towardsdatascience.com/author/benjaminnweke/)\nBenjamin Nweke is a Computer Science graduate and technical writer exploring AI, machine learning, and data science. He writes about building reliable AI systems, model evaluation, agentic architectures, and the practical challenges that appear when machine learning moves beyond experiments. 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