[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-hybrid-ai-model-tries-to-spot-corporate-tax-avoidance-signals":10,"sections":34},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":29,"feedback":33,"feedback_at":22,"cost_usd":33,"total_tokens":33},6806,"hybrid-ai-model-tries-to-spot-corporate-tax-avoidance-signals","Hybrid AI Model Tries to Spot Corporate Tax Avoidance Signals","A hybrid gradient-boosted-tree and neural-network model forecasts corporate tax avoidance signals from Korean firm data, but is no universal fix.","A new hybrid AI model claims it can flag which companies are quietly minimizing their tax bills, and it comes with an unusually honest scorecard on its own limits.\n\nResearchers built PaGNet, a two-branch system that pairs a LightGBM gradient-boosted-tree model with a neural network branch, then blends their outputs per target using a validation step rather than trainable fusion weights. They tested it on a panel of 1,754 Korean listed firms tracked from 2011 to 2024, across four different feature setups, and stacked it against six baseline models. For accrual-based tax avoidance measures, PaGNet raised explained variance by roughly 0.08 to 0.11 over the best baseline. Effective tax rate measures were messier: one metric leaned toward the neural branch, while another showed the model picking different branches depending on whether it was validating or testing.\n\nThis matters less as a tax enforcement breakthrough and more as a case study in what these systems can and cannot promise. A rolling-origin check found that on a distant time horizon, the supervised models actually lost to naive persistence, meaning a model that just assumes next year looks like this year.\n\nThe researchers themselves frame PaGNet as a proxy-aware diagnostic tool, not a universal upgrade over existing tabular models. Given how often financial AI tools get sold as one-size-fits-all solutions, that kind of self-imposed asterisk is worth noting on its own.","[\"ai\",\"tax-avoidance\",\"machine-learning\",\"corporate-finance\"]","2026-09-18T04:00:00.000Z","2026-09-18T17:32:30.591Z","2026-09-18T17:32:42.521Z","published",null,[],"ai",[24,26,27,28],"tax-avoidance","machine-learning","corporate-finance",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.20177",0,{"sections":35},[36,39,43,48,53,57,61,66,70,75,80,85,90,95],{"name":37,"slug":24,"count":38,"latest_published_at":18},"AI",4017,{"name":40,"slug":41,"count":42,"latest_published_at":18},"Security","security",653,{"name":44,"slug":45,"count":46,"latest_published_at":47},"Policy","policy",338,"2026-09-11T04:00:00.000Z",{"name":49,"slug":50,"count":51,"latest_published_at":52},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":54,"slug":55,"count":56,"latest_published_at":18},"Hardware","hardware",155,{"name":58,"slug":59,"count":60,"latest_published_at":18},"Science","science",121,{"name":62,"slug":63,"count":64,"latest_published_at":65},"Consumer Tech","consumer-tech",99,"2026-09-09T17:27:33.000Z",{"name":67,"slug":68,"count":69,"latest_published_at":18},"Dev Tools","dev-tools",77,{"name":71,"slug":72,"count":73,"latest_published_at":74},"Software","software",75,"2026-09-10T20:41:21.000Z",{"name":76,"slug":77,"count":78,"latest_published_at":79},"Startups","startups",55,"2026-09-09T23:14:29.000Z",{"name":81,"slug":82,"count":83,"latest_published_at":84},"Gaming","gaming",43,"2026-09-10T12:18:06.000Z",{"name":86,"slug":87,"count":88,"latest_published_at":89},"General","general",41,"2026-09-08T01:57:23.000Z",{"name":91,"slug":92,"count":93,"latest_published_at":94},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":96,"slug":97,"count":98,"latest_published_at":99},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]