How Financial Institutions can Use Genai

What do a bank, a hospital, and a logistics firm have in common? They’re all quietly experimenting with GenAI in ways that actually matter. Not to win headlines. Not to build shiny copilots. But to drive results. That’s what stuck with me while exploring Deloitte’s GenAI Use Case Navigator. https://lnkd.in/eskkGqH4 It’s not just a catalog of AI ideas, it’s a reality check. Because here’s what it reveals: ➤ GenAI’s biggest impact isn’t in customer experience fluff. It’s in fixing the unseen bottlenecks that drag businesses down. ➤ The most transformative use cases? Not the ones that sound fancy—but the ones that reduce manual effort, save time, cut cost. ➤ Think: claims intake, RFP responses, contract summarization, fraud detection, supply chain prediction. Real examples? ➡️ A global insurer used GenAI to automate underwriting analysis, reducing quote generation time from 5 days to 30 minutes. ➡️ A healthcare system used it to summarize complex patient histories before physician review, cutting admin time by over 40%. ➡️ A logistics company deployed GenAI to optimize route planning and fuel usage, saving millions in operational costs. ➡️ A government agency implemented GenAI to automate the review of grant applications, ensuring consistency and reducing cycle times. ➡️ A legal team used it to draft NDAs and review contract clauses—freeing up attorneys for higher-value work. ➡️ A finance team built a GenAI-powered dashboard that answers natural language queries about spend, variances, and forecast anomalies—no analyst needed. They’re not talking about “prompt engineering.” This is so 2023. They’re engineering out inefficiencies. They’re not building AI for the sake of it. They’re using AI to solve what’s broken, fragmented, or too slow to scale. Because. ChatGPT is NOT your strategy. AI is NOT your strategy. Your strategy IS to run your business better. Smarter. Leaner. Faster. AI's power depends on where and how you use it. Because in the end, it’s not about being an “AI-first company.” It’s about being a results-first company. So here's the question: What’s the real ROI of GenAI? The pilot… or the process it quietly replaces forever?

AI just made its move into financial services. Anthropic announced a new tailored offering: Claude for Financial Services. Let’s break it down. • Claude connects directly to your internal data stack: Snowflake, Databricks, S&P, PitchBook, FactSet, and more. • It’s not a consumer chatbot. It’s a task-specific analyst, tuned for high-stakes environment. • It doesn’t train on your data. Privacy and compliance are foundational. • Oh yeah, and it can do Monte Carlo simulations. Where it creates value: • Investment teams can analyze portfolios, trends, and risk exposures in real time, without toggling across 12 dashboards or waiting on data prep. • Compliance and audit functions can use Claude to summarize regulatory updates, track adherence, and flag anomalies, before the next quarterly fire drill. • Client-facing teams can generate custom pitch decks, scenario models, and account insights on demand, without pulling an associate off a deliverable.    For CFOs • Increase visibility into financial drivers by asking natural-language questions across systems and models • Pressure-test scenarios in real time using up-to-date financial and macro inputs • Generate investor-ready insights faster and more consistently For FP&A Transformation leaders • Automate recurring analysis cycles such as forecast variance, budget rollups, and board package creation • Embed Claude into planning workflows to assist with driver modeling, commentary, and contextualization • Scale insight delivery without increasing headcount For GenAI Transformation leads • Operationalize AI within high-stakes workflows without reengineering existing systems • Launch proof-of-concepts with measurable productivity impact in under 90 days • Build a business case grounded in time saved, accuracy improved, and risk reduced Real results: • AIG accelerated underwriting by 80% while increasing data quality from 75% to 90% • Norway’s NBIM saved over 213,000 hours in a single deployment with a 20% productivity lift across finance teams If you’re leading a team inside a Fortune 500 and wondering where to start: Identify high-friction, high-repetition tasks in finance, ops, or risk. Don’t wait for a firm-wide transformation plan. Start small with one workflow Claude could automate or accelerate. Pilot. Measure. Expand. ----------------------- Follow me for GenAI Transformation, Training, and News.

Mastercard's recent integration of GenAI into its Fraud platform, Decision Intelligence Pro, has caught my attention. The results are impressive and shows the potential of “GenAI in Advanced Business Applications”. As someone who follows AI advancements in Fraud across the FSI industry, this news is genuinely exciting. The transformative capabilities of GenAI in fortifying consumer protection against evolving financial fraud threats showcase the potential impact of this integration for improving the robustness of AI models detecting fraud. The financial services sector faces an escalating threat from fraud, including evolving cyber threats that pose significant challenges. A recent study by Juniper Research forecasts global cumulative merchant losses exceeding $343 billion due to online payment fraud between 2023 and 2027. Mastercard's groundbreaking approach to fraud prevention with GenAI integrated Decision Intelligence Pro is revolutionary. - Processing a staggering 143 billion transactions annually, DI Pro conducts real-time scrutiny of an unprecedented one trillion data points, enabling rapid fraud detection in just 50 milliseconds. - This innovation results in an average 20% increase in fraud detection rates, reaching up to 300% improvement in specific instances. As we consider strategic imperatives for AI advancement in fraud, this news suggests what future AI models must prioritize: - Rapid analysis of vast datasets in real-time, maintain agility to counter emerging fraudulent tactics effectively, and assess relationships between entities in a transaction. - By adopting a proactive approach, AI systems should anticipate and deflect potential fraudulent events, evolving and learning from emerging threats to bolster security. - Addressing the challenge of false positives by evolving AI models capable of accurately distinguishing legitimate transactions from fraudulent ones is vital to enhancing overall security accuracy. - Committing to continuous innovation embracing AI is essential to maintaining a secure and trustworthy financial ecosystem. #artificialintelligence #technology #innovation