Banks Need Hypergranualar Analytic Skills as Precision Strategies Take Over from Scale

The latest Global Banking Report 2025 from McKinsey on the sector’s future is pretty eye-opening, especially with all the buzz around AI and how banks are scrambling to keep up. The big idea they're pushing is "precision-based strategies," which basically flips the script on how banks have traditionally run things when big was beautiful.

This also pushes forward the urgency for inducting new skills like AI proficiency, with strategic, ethical acumen and data scientists skilled in hypergranular analytics. NTT DATA's 2025 survey also found that 58% of banking organizations have fully implemented generative AI in at least one function, compared to 45% in 2023.

According to the report traditional banks face fading tailwinds (e.g., high rates), competition from AI/fintechs, and eroding loyalty. Legacy strategies yield low productivity despite $600B annual tech spending and a 70% valuation gap versus peers. The shift to "precision swaps" prioritizes agility over brute force: AI-driven, real-time personalization replaces reactive standardization. Focused investments (not scattered "thousand flowers" projects) could lift returns on equity by up to 4 percentage points for pioneers.

Precision, as per McKinsey, goes beyond broad, one-size-fits-all strategies, as well as strategies that are simply “tailored”—for example, segmenting customers into broad groups and adjusting offers to cater to those groups. Precision means being data driven, targeted, hypergranular, and real time, enabling banks to focus resources where they create the most value. Even small steps ahead of competitors can trigger a positive cycle of growth and reinvestment, widening the performance gap over time.

McKinsey breaks this into a "precision toolbox" with four key areas:

• Technology: Instead of dumping billions into every shiny new gadget, banks pick and choose—say, zeroing in on agentic AI (that's AI that acts on its own, like an autonomous helper) for things like real-time fraud detection or automating customer service. They scale back on stuff that doesn't move the needle, like outdated digital projects that just add bloat.

• The new consumer: Banks move from lumping customers into big buckets (like "mass market" or "affluent") to treating each person as their own unique "segment of one." Using AI and data, they deliver hyperpersonalized stuff—imagine your bank app suggesting a loan tweak based on your exact spending habits or life events, building trust when loyalty is at an all-time low.

• Capital efficiency: No more big, vague reallocations of money across the whole balance sheet. Precision means drilling down to the micro level—product by product, client by client, even individual risk-weighted assets—to free up trapped cash and redeploy it where it earns the most. AI can run constant simulations to spot inefficiencies, like partnering with insurers to offload risks.

 New Skills in Demand for the Banking Sector

The precision era demands a workforce blending technical AI proficiency with strategic and ethical acumen to harness transformative trends effectively. AI literacy and prompt engineering will be foundational, enabling employees to deploy agentic systems for real-time personalization and risk monitoring, as banks scale from pilots to enterprise-wide use cases like DBS's 350+ AI applications. Data scientists skilled in hypergranular analytics—integrating transaction data with external signals for microsegmentation—will be essential for "segment of one" strategies, requiring expertise in machine learning models that evaluate non-traditional credit signals to approve 44% more borrowers at 36% lower rates.

Capital optimization roles will need advanced modeling for line-by-line risk-weighted asset (RWA) analysis under Basel IV, using AI simulations to unlock trapped capital and simulate scenarios, alongside regtech proficiency for stablecoin compliance and cyber resilience amid rising fraud threats. M&A specialists must develop micromarket valuation skills, focusing on capability gaps like niche AI tech, while agile change managers facilitate cultural shifts from scale to precision through cross-functional teams.

Soft skills in ethical AI governance, including bias mitigation and privacy compliance, will address trust erosion, with human-AI collaboration emphasized for high-value interactions like advisory services. In emerging markets, mobile-first design and inclusive finance expertise will cater to digital natives and generational wealth transfers. Upskilling via no-code tools could initially cost 1-2% of revenues but yield ROTE gains, prioritizing continuous learning in generative AI for fraud detection (doubling rates) and customer chatbots (reducing call volumes by 55%).