Finanzdienstleistungsbranche

As Baby Boomers continue to retire, and as AI transforms entry-level roles, task-based work is disappearing. Human-AI collaboration is redefining where human influence and judgment matter most. 

Slalom’s research reveals that, by 2028:  

• 15% of routine decisions in the financial services workforce will be automated.

• 70% of task execution functions will run through AI systems supervised by humans. 

Meanwhile, over the next decade, more than one-third of financial advisors, who control about 40% of industry assets, are expected to exit the industry or retire. This exit will make way for more human-AI collaboration as a new generation of advisors operates with more comfort, engagement, and skill using AI tools.

Slalom’s research found that financial services leaders are more comfortable with human-AI collaboration than many of their peers in other industries.

While climate intelligence is already mission-critical for insurers and large banks, many financial services leaders still see it as an emerging priority—or one they’re not ready to address due to data model constraints, organizational silos, and regulatory uncertainty.

But climate intelligence isn’t an ESG branding term. It’s an analytical edge that helps you mitigate risk against stranded assets and uninsurable or unpredictable exposures. 

Forward-looking banks and insurers are using granular climate data to capture outsized returns via “climate alpha” first-mover advantages. With the power to price climate risk earlier and more precisely than the competition, these brands are outperforming peers through dynamic risk-adjusted pricing, predictive underwriting, and climate-aligned capital allocation. 

The most performant, profitable, and adaptable firms are moving climate intelligence from emerging technology into a core capability. Insurers are moving beyond payout to prevention, offering incentives for resilience investments and using parametric triggers to settle claims instantly. Laggards will still treat climate intelligence as a compliance checkbox or ESG exercise that leaves them exposed on a spectrum from shrinking margins to catastrophic loss.

Without liquid, interoperable yet governed data, AI outcomes are unreliable. Nor can customer-facing innovation scale. Anticipatory enterprises are shifting from static data stores to interoperable, productized, consented data flows that move securely across institutions and partners. 

How are they pulling it off? Leading firms are operationalizing clean data contracts, standardized APIs, and shared services that fuel real-time and autonomous AI, personalization, and fraud/treasury visibility. 

Networked intelligence is a new frontier in financial services. Banks that replace siloed, legacy systems with modern, interconnected, real-time data flows enable faster fraud detection and more valuable personalized experiences that scale. In capital markets, trades get faster and smarter because connected platforms and networks instantly share pricing, risk, and liquidity information across partners and desks. In insurance, claims and underwriting reduce loss and improve customer experience with proactive risk alerts and shorter cycle times thanks to links between internal data (e.g., policy, history) and external data feeds (weather reports, IoT data). 

Across the industry, the shift to networked intelligence starts with interoperability. This shift isn’t just technical. It’s strategic. Firms that embrace it will gain GTM speed and agility, customer trust, and a sustainable edge over competitors.

Leaders will transform data into liquid, governed, productized services that power AI and real-time operations. Laggards will stay stuck with fragmented data and fragmented outcomes.

The financial services business model is tilting. It’s not just about selling end products like accounts and policies. And it’s not about treating infrastructure as sunk costs.

Leading institutions are taking what used to be cost centers—fraud checks, payment systems, and compliance tools—and turning them into revenue-generating assets. By packaging these capabilities with clear service guarantees and turning them into APIs or white-label offerings, firms are poised to unlock new partnership models and monetize the infrastructure behind everyday transactions.  

The composable, modular banking market is on the cusp of staggering growth, expanding from USD $3.7B in 2024 to $36.2 billion by 2033.

How are banks measuring ROI on modernization efforts that decouple monoliths into composable services?

Financial services firms that remain monolithic will lose to competitors (including fintechs, neobanks, and tech firms) building on composable rails. Laggards will grasp at shrinking margins, while leaders who productize infrastructure into reusable, modular, monetizable services with clean APIs and clear SLAs strengthen their strategic position. 

Learn more about Slalom’s mainframe and application modernization.

The industry is moving beyond automation for efficiency and shifting toward autonomy that anticipates decision points and acts. Just ask Amex. With AI agents, real-time data, and governance policies in place, systems can make self-directed adjustments—such as pricing, fraud detection, collections—while maintaining compliance. This creates notable cost-to-serve and speed-to-market advantages.

But autonomy without guardrails runs the risk of reputation and compliance fallout as regulators require proof of control and explainability. So institutions will need to design Agent Control Planes—like policies, monitoring, and kill switches—to scale autonomy responsibly. 

What does autonomy look like across financial services?

Autonomy is the new competitive frontier. Leaders that scale self-directing agents with strong governance will deliver speed and efficiency gains without sacrificing compliance or transparency with regulators. Laggards still stuck at automation will face widening cost gaps and erode their own market relevance.