The State of AI and Data in Financial Services

The financial services industry’s digital transformation and innovation goals are diverse – reflecting varying regulations, customer expectations and market realities. That said, artificial intelligence (AI), generative artificial intelligence (GenAI) and data are common goals – and challenges – that organizations across the financial services industry are contending with.  

AI and GenAI have already proven to be effective at optimizing a wide variety of processes for banks, credit unions and companies that offer different financial products and services. AL solutions can improve customer interactions, speed up service, support lending practices and empower risk management strategies. For firms that specialize in investment opportunities and stock markets, AI supports trading by streamlining information and increasing visibility, while minimizing risks. But AI isn’t just about efficiency, automation, speed and convenience. In financial services – where compliance and security are paramount – AI’s delivers precision and monitoring capabilities that strengthen fraud detection, protect privacy and ensure regulatory compliance.  

What is driving AI innovation? Data. Vast amounts of clean, comprehensive datasets that train AI models so companies can operationalize them effectively. This is why any AI initiative must first start with an evaluation – and probably upgrade – of data collection, storage and governance processes. Regardless of the region of operation, business needs and client base, British, European and American companies in financial services are increasingly turning to AI and data to increase efficiency, boost offers, enhance customer service and boost security.Read on to learn more – you can download our free ebook about AI and data in financial services here.

Snapshot of AI implementation in financial services

In the UK(1) 

75% of firms are already using AI, with a further 10% planning to use AI over the next three years 

1/3 of all AI use cases are third-party implementations 

55% of all AI use cases have some degree of automated decision-making 

41% are using AI for optimization of internal processes 

Business areas with the highest percentages of implementations:  

human resources 65% 

risk and compliance 64% 

operations and IT 56% 

In the EU(2) 

85+% of institutions are actively using AI 

40+% of institutions are actively using GenAI 

80+% of institutions are actively using Big Data analytics 

69% of banks are developing proprietary AI models or systems 

39% of banks are outsourcing the development of AI models or systems 

Over the next three years, 18% of firms plan to invest between .25% to <.50% in GenAI, while 58% plan to invest between 0% and <.25% (as percentage of equity). 

In the US(3)(4) 

78% of financial firms are implementing GenAI for at least one use case 

86% of financial firms expect a significant or moderate increase in their model inventory due to GenAI adoption 

$100 billion USD will be invested in AI by American companies in 2025 

Strategic challenges in global financial services

It’s clear that financial institutions across the UK, EU and US are navigating a period of accelerated change.  

Organizations need to simultaneously:  

• comply with increasingly fragmented data privacy laws (such as GDPR, CCPA and DORA) 

• deliver hyper-personalized customer experiences across digital channels 

•  responsibly integrate emerging technologies like AI and ML into legacy systems.  

These pressures are reshaping not only compliance and IT functions, but also strategic priorities across the financial sector. 

Regulatory complexity is intensifying  

Banks and financial firms face mounting pressure to comply with evolving and fragmented frameworks, such as FCA and DORA in Europe, SEC and CCPA in the US, and ESG-related disclosures globally. Meeting these standards requires continuous investment in compliance infrastructure, often at the cost of innovation. As the financial and reputational consequences of non-compliance are growing, companies need to align with applicable legal frameworks or face crippling fines. 

Trust remains fragile and essential 

Public confidence is easily eroded by data breaches, misconduct or shady practices. Across all major markets, customers expect transparent, ethical and personalized digital services. FinTech challengers continue to raise the bar by delivering more intuitive and trust-centric experiences, which means traditional banks need to adapt, or become obsolete. 

Legacy systems constrain agility 

Many institutions still rely on outdated core infrastructure, siloed data, and cautious governance models. These hinder the ability to respond quickly to market changes, integrate new technologies, or scale innovation effectively, especially compared to tech-native competitors. 

Scalability and integration remain major barriers 

Decades-old systems often struggle with real-time processing, secure API integration and cloud transformations. Migrating to modern platforms is complex, especially when critical services and compliance requirements must be maintained throughout. 

Cybersecurity threats are expanding 

Financial institutions are prime targets for sophisticated cyberattacks. Managing data securely, while enabling openness via APIs, Open Banking, or digital channels, requires continuous investment in resilience, monitoring and skilled talent across all regions. 

Practical applications of AI in financial services

AI, particularly GenAI, is rapidly transforming the financial industry, as it offers solutions that significantly enhance both operational efficiency and customer experience. McKinsey (5) estimates that full implementation of GenAI could contribute an additional $200–$340 billion USD in annual value to the banking sector alone. This immense potential is driving significant investment, with global spending on AI in banking projected to grow from $21 billion in 2023 to $85 billion by 2030 (6). 

The following are key areas where AI applications are revolutionizing financial services: 

Financial reporting analysis with AI  

AI models possess the advanced capability to summarize and extract information from lengthy documents – a function that was historically very limited. For instance, a generative model can rapidly summarize a 100-page regulatory filing or years of trading data to produce a plain-language report on a portfolio’s risk exposures. ABN Amro, for example, has utilized GenAI to scan and summarize customer call transcripts and other documents, which, in one case, improved the productivity of contact center staff by 25% by eliminating manual note-taking. (6) AI serves as an intelligent intermediary, translating complex raw data into simple, actionable language for decision-makers or clients. It can also automate the analysis of historical data, social media content, news and analyst reports to identify market trends and shifts in investor sentiment. 

AI-powered document review and management  

AI significantly automates and accelerates tasks traditionally performed manually in document processes – improving efficiency in both back-office and mid-office operations. Wells Fargo has deployed an agentic AI system for loan underwriting that can retrieve and extract relevant data from documents, perform calculations and re-underwrite loans in minutes – a process that previously took days of human effort, with human oversight for final judgment calls. (7) Furthermore, GenAI can effectively analyze vast amounts of unstructured data, such as press releases, corporate disclosures, or call transcripts, to generate summaries and valuable insights for stakeholders. In expense management, AI automates the capture and population of purchase data from company cards, which streamlines operations and reduces errors by eliminating manual entry from receipts. A notable example of this can be seen in SAP Concur’s partnership with Mastercard. 

Knowledge management through intelligent search  

AI is transforming how employees access information and manage knowledge within organizations. J.P. Morgan’s Asset & Wealth Management division uses an AI “Coach” tool that allows private bankers to retrieve information 95% faster, enabling advisors to dedicate more time to clients. (8) This assistant can instantly compile information on client portfolios and market data, and even draft responses to client inquiries. Morgan Stanley’s Wealth Management division implemented the AI @ Morgan Stanley Assistant, a GPT-4-powered tool trained on the firm’s extensive internal knowledge base (over 100,000 research reports). This tool provides financial advisors with instant access to tailored insights for clients. JPMorgan Chase has also rolled out an internal GenAI platform, the “LLM Suite,” to approximately 200,000 employees, to assist with tasks like drafting emails, presentations and summarizing documents. (9) 

Real-time ML platform for fraud detection and credit scoring 

AI also supports risk and compliance departments, by creating synthetic examples of fraudulent transactions or suspicious trading patterns, which can be used to train more effective detection algorithms. The result? Financial institutions can stay ahead of bad actors.  

Here are some examples: 

Feedzai leverages GenAI to simulate fraud scenarios, enhancing its system’s ability to recognize new fraud patterns. One large European bank saw a 60% reduction in false positives and a 20% increase in actual fraud detection rates after integrating GenAI-enriched training.  

Bank of America attributes a 45% reduction in credit card fraud losses, amounting to an estimated $500 million USD in 2024, to its AI-enhanced fraud detection systems. (10) Mastercard also uses AI-powered cybersecurity to combat real-time payment scams, preventing over $35 billion in fraud losses in the last three years. (11)  

MYbank, a digital bank from Ant Group, employs a “310 lending model” that enables small business owners to apply for collateral-free loans in three minutes, with approval in one second and no human interaction. This model minimizes risk by analyzing monthly sales and repayment patterns.  

AI & ML for regulatory compliance consulting  

GenAI can significantly reduce manual efforts by assisting in the rapid drafting of risk reports and compliance documents, as well as by parsing complex legal and regulatory texts. For example, Citi uses GenAI to prepare new projects for compliance review, generate project summaries and identify applicable regulations.  

AI investment advisory assistant  

By acting as a virtual financial coach, AI can proactively support clients with financial planning. Fintechs are developing personal finance chatbots that analyze user spending and saving patterns to generate tailored advice. GenAI can simulate advisory conversations at scale for millions of customers – providing individualized insights and product recommendations when combined with customer financial data. Wealth managers at UBS and HSBC are streamlining market research and reducing time spent on data synthesis by utilizing AI to analyze market data, assess risk and tailor investment strategies for clients. 

Finding a partner with technical experience and domain knowledge

Financial institutions around the world are investing in AI, especially as a means to boost operational efficiency, increase security and enhance customer experience. Aside from initial investment and technical choices that need to be made, companies need to overcome strategic challenges that accompany AI implementation: staying complaint with data privacy laws, meeting ever-increasing demands for personalized experiences and integrating new technologies into existing systems. That’s why companies reach out to Software Mind. With years of industry experience and a partner ecosystem that includes Databricks, Backbase, ServiceNow, Bloomreach and others, Software Mind provides technical and business experts that handle all aspects software development. Contact our experts and learn how we can support your digital strategies. 

FAQ

What are the AI adoption rates for companies in the financial services industry?

In the UK, 75% of firms are already using AI, with a further 10% planning to use AI over the next three years. Additionally, 41% are using AI for optimization of internal processes. In the EU, 85+% of institutions are actively using AI and 40+% of institutions are actively using GenAI. By 2028, 18% of firms plan to invest between .25% to <.50% in GenAI, while 58% plan to invest between 0% and <.25% (as percentage of equity). In the US, 78% of financial firms are implementing GenAI for at least one use case and $100 billion USD will be invested in AI by American companies in 2025 

Where are financial institutions implementing AI?

Both AI and GenAI are effective at optimizing a range of processes for banks, credit unions and companies that offer different financial products and services. AL solutions can improve customer interactions, speed up service, support lending practices and empower risk management strategies. For firms that specialize in investment opportunities and stock markets, AI supports trading by streamlining information and increasing visibility, while minimizing risks. But AI isn’t just about efficiency, automation, speed and convenience. In financial services – where compliance and security are paramount – AI’s delivers precision and monitoring capabilities that strengthen fraud detection, protect privacy and ensure regulatory compliance. 

What challenges need to be overcome when integrating AI solutions into operations?

Aside from the initial investment and retraining of staff that needs to accompany any AI integration, companies in financial services must adhere to relevant  

 data privacy laws (such as GDPR, CCPA and DORA) and other financial regulations. As with any digital transformation, companies need to responsibly integrate AI solutions into existing systems in a way that is transparent and does not disrupt ongoing operations. Companies need to also strike a balance between delivering personalized user experience with security. 

What role does data play in AI innovations?

Data plays an integral role in driving AI innovation – both in terms of quality and quantity.  In order for AI models to effectively work, companies must provide clean and comprehensive datasets to train them. This is why any AI initiative must first start with an evaluation – and probably upgrade – of data collection, storage and governance processes. 

What are practical examples of AI use cases?

AI has proven to be effective at analyzing, summarizing and extracting information from financial reports – turning raw data into actionable insights for decision-makers. AI-powered automation can support document reviews and the collection of documents. Other areas include intelligent search functionalities, drafting of risk reports and compliance documentation and acting as an investment advisory assistant.