Raw gams

Might want to use Anaconda to decompile those if they're Games Factory 2 / MMF2 era games. #9 Hope this ain't too much of a necropost (last post was January of this year at least), but what happens/does it mean if the Clickteam file gets a "stop status encountered" error when trying to extract? #10 Hope this ain't too much of a necropost (last post was January of this year at least), but what happens/does it mean if the Clickteam file gets a "stop status encountered" error when trying to extract? Doesn't sound good though, maybe the .MFA file is incompatible with that version? #11 Aha, Beavis & Butthead, another man of culture! ;)It is quite a sporadic error, as there's no pattern as to which .cca/TGF .gams it seems to pop up on, out of the files I've tried the Extractor on. #12 Aha, Beavis & Butthead, another man of culture! ;)It is quite a sporadic error, as there's no pattern as to which .cca/TGF .gams it seems to pop up on, out of the files I've tried the Extractor on. that's what I'm gonna guess then, I don't think anaconda can decomp the absolute latest stuff afaik?

A growing roster of banks and fintechs are deploying XAI to balance predictive power and transparency. Some examples:Discover Financial Services: The credit card giant uses EBMs to predict default risk and explain model decisions to customers. Compared to a traditional FICO scorecard, Discover found EBMs achieved a 5-10% lift in accuracy while still providing reason codes like "Too many recent missed payments."Zest AI: This fintech‘s Model Management System includes built-in SHAP dashboards to help lenders identify and monitor the key variables driving credit model decisions. In a 12-month pilot, Zest‘s toolkit helped one subprime auto lender increase approvals by 30% while keeping defaults flat. H2O.ai: H2O‘s Driverless AI platform automates the process of fitting interpretable models like EBMs and GAMs and offers a range of XAI tools to debug black-box models. Several banks have used the software to boost credit model accuracy and satisfy regulatory requirements.While these early adopters are seeing promising results, operationalizing XAI in high-stakes lending is not without challenges. Generating explanations in real-time at scale remains computationally intensive, and there are still open questions around which XAI techniques are most appropriate for different use cases. More fundamentally, lenders must grapple with how to translate raw XAI outputs into consumable, actionable insights for different personas, from loan officers to compliance teams to consumers. Building intuitive user interfaces and integrating XAI into existing decisioning workflows requires cross-functional collaboration.A Call to Action for Responsible AIAs the lending industry hurtles into an AI-first future, explainability is no longer a nice-to-have – it‘s table stakes for responsible innovation. By proactively embracing XAI, financial institutions can unlock the full potential of machine learning without sacrificing transparency or accountability. Here are some key considerations for lenders looking to embed explainability into their model governance frameworks:Set clear policies: Develop guidelines on when interpretable-by-design models are

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