One topic has dominated every technology discussion across the financial services and insurance industries for well over a year—and it is going to be even more prevalent in 2025.
Mass investment in AI integration is now moving well beyond the pilot phase, and the impact of its proliferation will start tangibly reshaping FSI in the coming year—for both good and ill. Here are a few snapshots of what AI will be driving in 2025:
Retail Banking, Including Lending and Payments
AI-driven personalization will raise privacy concerns and regulatory scrutiny. By the end of next year, retail banks will leverage AI to offer hyper-personalized products and services. However, the extensive use of customer data will trigger heightened privacy concerns, prompting regulators to impose stricter data usage and consent laws.
Real-time fraud detection will also become a competitive necessity amid rising cyber threats. Banks adopting advanced AI for instant fraud detection in payments will gain a significant edge, and institutions lagging in AI integration will face increased cyber attacks, leading to financial losses and reputational damage. The sophistication of AI-driven cyber threats will compel banks to significantly increase their cybersecurity budgets, focusing on AI-based defense mechanisms and robust data protection protocols.
Expect to see mandatory explainable AI in lending decisions as regulators will require banks to use explainable AI models to prevent biases in lending. This will force banks to overhaul their AI systems to ensure transparency and fairness, impacting their data management strategies.
Wealth and Asset Management
The proliferation of AI-driven robo-advisors is set to disrupt the wealth management industry, forcing firms to reassess their human capital and value proposition amid clients’ growing trust in automated services. This shift will coincide with enhanced regulatory oversight of AI algorithms. Regulators are expected to implement stringent audits of AI algorithms used in asset management to ensure compliance and prevent market manipulation, increasing the complexity and cost of data management.
At the same time, wealth management firms will face heightened cybersecurity threats, mirroring trends across the financial services sector. These companies will become prime targets for cybercriminals, with any significant breach resulting in loss of client trust, legal penalties, and a push for more robust cybersecurity frameworks.
Efforts to monetize client data through analytics will also face challenges. Privacy concerns are likely to spark backlash, resulting in stricter regulations and potential legal challenges. Despite these obstacles, a shift towards sustainable investing via AI analytics is emerging. AI will enable a more precise analysis of ESG factors, leading to a significant shift in investment strategies towards sustainable assets. However, it will also raise questions about data reliability and standardization.
Property and Casualty Insurance
Insurers adopting AI for real-time data analysis in underwriting will outperform competitors, but may encounter regulatory concerns regarding data privacy and algorithmic bias. At the same time, the rise of sophisticated, AI-driven insurance fraud will force companies to invest in equally advanced AI detection systems, straining budgets and requiring new data management approaches.
Cyber insurance is emerging a dominant market segment and due to increasing cyber threats, driven by escalating cyber threats. While demand for cyber insurance is expected to grow, insurers will struggle with underwriting risks in an area lacking historical data, complicating data management.
Regulators will also mandate the inclusion of climate data in risk assessment models as regulators will require P&C insurers to incorporate climate change projections into their risk models. This will significantly increase data management burdens and drive the adoption of advanced AI analytics to handle these complex requirements.
Additionally, stricter privacy regulations will impact claims processing efficiency. Enhanced privacy laws will restrict the use of personal data in claims processing, forcing insurers to find a balance between efficient service and compliance, potentially leading to slower settlement times.
Private Equity and Private Credit
In 2025, firms utilizing AI for rapid due diligence will have a competitive advantage yet may face regulatory scrutiny over data sources and the potential for overlooking nuanced risks. Investors are intensively evaluating the cybersecurity posture of target companies, as the acceleration of AI-driven threats means that poor data protection measures could result in deal cancellations or reduced valuations.
What’s more, regulatory bodies are intensifying their focus on AI-based credit scoring. Regulators will demand transparency in AI credit models to combat discriminatory lending practices, compelling firms to adjust their data management and AI systems accordingly. That said, heavy reliance on AI for investment decisions may result in biased outcomes, leading to legal disputes and harming the firm’s reputation among investors and the public.
Adding to these challenges, stricter data privacy regulations are reducing the availability of alternative data for AI models. This will push private equity and credit firms to seek new ways to gain insights without violating laws.
A Year of Challenges
In 2025, the finance sector will broadly start displaying many of the amazing operational efficiencies and capability gains well-implemented AI really can deliver. But it will also be a year where its rapid integration into financial services will have real consequences.
AI use in financial services has already outpaced the speed at which regulations are developed, leading to a complex landscape where institutions will struggle to stay compliant amid evolving legal requirements and potential penalties.
As regulatory bodies catch up, they will begin enforcing strict transparency and explainability standards for AI algorithms in financial decision-making, as well as regional and global data privacy regulations that will significantly restrict how financial institutions collect, store, and use customer data. Firms must be prepared to overhaul their data management practices to ensure AI models are interpretable, fair, and free from bias. Existing AI models reliant on extensive datasets will be challenged, pushing firms to adopt new methods like synthetic data generation and federated learning. Such eventualities will impact operational efficiency.
All the while, the industry will face a new wave of sophisticated cyberattacks, driven by AI and targeting vulnerabilities in financial systems. This will force companies to invest heavily in advanced cybersecurity measures — ironically including AI-based defense mechanisms and AI-driven comprehensive data protection protocols.
There is no putting this genie back in the bottle. In 2025, AI use in financial services won’t be a differentiator. It will be a requirement for survival in a landscape that it has already irreversibly altered.
