This blog draws insights from my conversations with Financial Services leaders at NVIDIA’s AI in FS meetup, along with 1:1 discussions with our customers and stakeholders—plus firsthand experience from our own AI initiatives in the space
The Financial Services (FS) and Insurance industries are undergoing a seismic shift—not just in how they operate, but in how they think. The convergence of AI and GenAI is democratizing intelligence, embedding it directly into Systems of Record (SOR), Systems of Analytics (SOA), and Systems of Engagement (SOE). But the real game-changer? The rise of Systems of Intelligence (SOI)—where AI doesn’t just assist but orchestrates decision-making in real time.
This isn’t just about automation—it’s about hyper-efficiency, hyper-personalization, and hyper-risk-awareness. Let’s explore how this transformation is unfolding with real-world examples.
- From static data to intelligent Systems of Record
Traditional Systems of Record (SOR)—core banking platforms, policy administration systems, and transaction ledgers—were once passive repositories. Today, AI is turning them into active intelligence hubs.
Example: AI-powered fraud detection in real time
- Problem: Banks lose billions to fraud, often detecting it too late.
- AI Solution: Machine learning models embedded directly into transaction systems analyse patterns in real time.
- Impact: JPMorgan Chase’s Onyx uses AI to detect fraudulent transactions before they settle, reducing false positives by 30%.
- Temenos – a core banking solution – is integrating AI agents into its product stack to accelerate the launch of new banking products, reducing timelines from months or weeks to just days
- Systems of analytics: from descriptive to predictive (and now prescriptive)
Traditional Systems of Analytics (SOA) provided hindsight—what happened. AI now offers foresight—what will happen—and prescriptive intelligence—what should be done.
Example: GenAI in underwriting & risk assessment
- Problem: Insurers spend weeks manually underwriting policies.
- GenAI Solution: Solutions like Intelligent Document Processing analyse thousands of data points (social signals, IoT data, historical claims) to approve policies in seconds.
- Impact: 90% faster underwriting with lower risk mispricing.
- Systems of engagement: hyper-personalized, AI-driven customer experiences
Banks and insurers once treated customers as segments. Now, Systems of Engagement (SOE)—chatbots, apps, and digital platforms—leverage AI to treat each customer as an individual.
Example: AI-powered wealth management
- Problem: Traditional advisors can’t scale personalized advice.
- AI Solution: Morgan Stanley’s AI @ Work Assistant, LSEG earnings calls assistant uses LLMs to analyse client portfolios, market trends, and even personal goals to offer tailored advice.
- Impact: Advisors spend 50% less time on research, focusing instead on high-value client interactions.
- The Future: Systems of Intelligence (SOI) – where AI orchestrates business
The next frontier? Systems of Intelligence (SOI)—where AI doesn’t just sit beside business processes but embeds intelligence into every decision.
Example: Autonomous claims processing in Insurance
- Problem: Claims processing is slow, manual, and prone to errors.
- SOI Solution: AI models (computer vision + NLP) assess damage from photos, cross-check policy details, and approve claims without human intervention.
- Impact: Tractable’s AI processes claim in minutes instead of days, cutting operational costs by 40%.
Example: AI-driven dynamic pricing in banking
- Problem: Static loan pricing misses real-time risk fluctuations.
- SOI Solution: AI adjusts interest rates in real-time based on macroeconomic shifts, credit behaviour, and even news sentiment.
- Impact: Klarna’s dynamic pricing AI has reduced defaults by 15% while optimizing approval rates.
The big shift: democratizing intelligence
What makes this revolution unique? Democratization. AI is no longer confined to data scientists—it’s in the hands of:
- Loan officers using AI to assess credit risk in seconds.
- Call centre agents guided by real-time AI prompts during customer calls.
- Claims adjusters leveraging GenAI to draft reports automatically.
The result? Faster decisions, lower costs, and experiences so personalized they feel human.
Conclusion: The AI-first financial institution
The future belongs to FS firms that don’t just use AI but bake it into their operational DNA. The winners will be those who:
- Embed intelligence into core systems (SOR → SOI).
- Replace batch analytics with real-time AI-driven decisions.
- Turn every customer interaction into a hyper-personalized experience.
The question isn’t if AI will transform Financial Services—it’s how fast you’ll adapt before competitors outpace you.
Are you building a System of Intelligence—or just watching from the sidelines?
Your bank’s ‘AI strategy’ is already outdated.
Here’s why: The real disruption isn’t just chatbots or fraud detection—it’s AI silently rewriting the DNA of financial services.
Imagine:
- Loan approvals that self-optimize based on real-time market shocks.
- Insurance claims that auto-settle before the customer even calls.
- Trading desks where AI agents negotiate with each other.
This isn’t sci-fi. It’s Systems of Intelligence—and they’re making legacy tech stacks look like fax machines.
The trillion-dollar question: Is your org building an AI-native future… or clinging to spreadsheets?