AI in financial services: Shaping the future of finance

Generative AI is the key technology shaping the future of AI in finance. Almost half of financial services organizations already leverage generative AI and large language models: 43% and 46%, respectively. 

Generative AI in banking can power: 

• Intelligent coding co-pilots to speed up digital transformation 
• Chat-your-data solutions to democratize enterprise data access 
• Synthetic data generation to train AI and ML models 
• Report generation and summarization for risk and compliance functions 
• Personal virtual assistants for customers 
• Hyper-personalized sales and marketing campaigns 
• Automated insurance claims report generation 
• Virtual damage rendering for insurance claim processing 

That said, generative AI is inching closer toward the phase of the trough of disillusionment, according to Gartner’s 2024 AI Hype Cycle. Gartner analysts, however, expect the technology to provide transformational benefits to adopters and reach wide adoption in two to five years. 

AI use cases in financial services are varied, spanning from fraud protection and enhanced risk management to hyper-personalized customer experiences and employee productivity tools. 

Financial crime prevention 

Fraud schemes led to $485.6 billion in losses in 2023 alone. The same year, $3.1 trillion in illicit funds moved through the global financial system. 

As financial crime poses both reputational and financial risks, it’s no wonder why 90% of financial organizations consider preventing them a top priority. 

Leveraging data analytics to identify and prevent financial crime is the leading opportunity for improving efficacy on this front. In fact, 65% of organizations plan to pursue this avenue, and 58% want to invest in AI to ramp up their financial crime and fraud detection capabilities. 

Cybersecurity 

Data breaches can cost you dearly: An average one incurs $4.88 million in losses, representing a 10% year-on-year increase compared to 2023. 

Staying one step ahead of attackers remains a top risk priority for financial services organizations. Yet, some areas still require improvement. They include third-party security, metrics and reporting, and access management. 

 

 

This is yet another application of artificial intelligence in banking and financial services: Identifying threats, maintaining real-time threat intelligence, and automating incident response. 

AI use cases in the cybersecurity domain include: 

• Identity and access management: Monitoring log-in activity; detecting suspicious behavior to predict potential account takeover; responding with a forced password reset and 2FA 
• Endpoint security and management: Identifying all endpoints; ensuring they remain up to date; detecting malware 
• Cloud security: Providing visibility into security vulnerabilities across multiple clouds and providers 
• Threat detection: Detecting anomalous behavior across apps, user accounts, and endpoints; alerting the team or responding automatically to the threat 
• Information protection: Identifying and labeling sensitive data; detecting potential data theft 
• Incident investigation and response: Identifying the most useful information for incident investigation and response for the cybersecurity team 

Thanks to advances in Gen AI, modern cybersecurity tools can also: 

• Synthesize data into recommendations and insights to facilitate incident investigations 
• Generate incident reports and cybersecurity presentations 
• Analyze existing codebase for security vulnerabilities and provide suggestions on removing them 
• Answer questions on incidents or cybersecurity measures in easy-to-grasp natural language 

Employee productivity tools 

Finally, AI in banking and finance can help employees become more productive across functions, from risk and compliance and customer service to marketing and sales. 

Gen AI tools in particular are at the forefront of innovation in this area. Applications of generative artificial intelligence in finance in this area are ubiquitous: 

• Summarizing lengthy documents, such as new regulations and reports (e.g., for accounting research, competitor analysis, and financial reporting) 
• Providing response suggestions to customer support agents in real time, boosting productivity as a result 
• Generating code snippets and suggesting improvements to existing code as part of AI-powered code review (e.g., Goldman Sachs is experimenting with this business case) 
• Extracting data from unstructured sources (e.g., emails), classifying it, and providing response suggestions 

However, Gen AI isn’t the only AI technology delivering efficiency gains: 

• Computer vision capabilities can extract data from visual sources (images and videos) to automate, for example, damage appraisal in insurance claims or ID verification during digital account opening 
• Intelligent document processing involves data extraction from structured and unstructured sources and its automated verification (e.g., in loan servicing) 
• Speech recognition can power more effective customer support triaging for call centers 
• Predictive analytics can determine the next best action during customer interactions, marketing campaigns, and investment decisions 

AI solutions for finance come with a wide range of potential benefits, from improved customer experiences and better operational efficiency to faster decision-making. 

Enhanced operational efficiency 

New operational efficiencies are among the most commonly cited benefits of AI in finance. The reason is simple: AI models can automate traditionally manual banking processes, such as data verification during account opening or creating risk and compliance reports. 

Improved customer experiences 

With AI-powered personalization, virtual assistants, and intelligent automation that expedites back-office processes, customers can enjoy smoother, frictionless experiences. As a result, leveraging AI and ML in finance can help you increase customer lifetime value, reduce churn, lower acquisition costs, and more. 

Mitigated risks 

Leveraging AI in finance can protect your organization from reputational and financial losses stemming from missed financial crime and non-compliance with more accurate fraud detection and a more robust AML and KYC framework. However, using AI can represent a risk in and of itself, which is imperative to address. 

New revenue streams 

In some financial services industry sectors, AI is already powering qualitatively new operating models, products, and services. For example, AI powers usage-based car insurance that calculates premiums based on driver behavior. 

Faster decision-making 

With chat-your-data solutions and powerful data analytics, AI in financial services can enable faster decision-making across business functions. Predictive modeling in particular can inform decisions regarding risk mitigation strategies, loan and insurance underwriting, and more. 

Competitive advantage 

AI can enable you to offer superior customer experiences, reduce operational costs via efficiency gains, and launch innovative AI-powered products and services. All of that translates into a competitive edge that AI pioneers gain over their slower counterparts. 

As a result, employees can find the right document section to answer their questions in 38.3ms, with over a million vectors searched. At the same time, the RAG tool allows users to get responses to complex queries (e.g., pricing comparison for a specific policyholder profile) by posing a single question. 

Regulators around the world have turned their attention to AI use, both in finance specifically and across industries. In the European Union, the passing of the Artificial Intelligence Act marked a new era in AI regulations. In the U.S., state regulators are working to protect consumers against algorithmic bias and other AI risks. 

Your AI implementation strategy also has to be in line with other regulatory requirements on consumer privacy (e.g., HIPAA, GDPR, CCPA), cybersecurity (e.g., DORA), and market conduct. 

Mitigation strategies: 

• Review all regulatory requirements that may impact AI model development and rollout 
• Ensure robust AI governance 
• Adopt explainable and responsible AI principles 
• Establish an AI ethics board and conduct regular reviews 

Potential errors and bias 

While hallucination is a top concern for generative AI models, traditional AI and ML models aren’t unbeatable when it comes to mistakes and bias. Since AI in banking and finance deals with sensitive data and can impact customers’ lives, preventing these errors and biases is crucial. 

Mitigation strategies: 

• Ensure the training datasets are diverse and representative 
• Regularly review the models for bias and error and fix any revealed issues 
• Apply anti-bias techniques to training (e.g., re-weighting, adversarial training) 
• Educate end users on the limitations of AI tools 
• Keep a human in the loop 

Data challenges 

While AI and ML models can rival human intelligence in many aspects, they’re only as good as the data used to train them. However, data silos, privacy issues, and insufficient volumes all pose substantial challenges in obtaining enough quality data for training models. Data issues are the leading challenge for financial institutions, with 38% facing them. 

Mitigation strategies: 

• Protect sensitive data with robust cybersecurity measures to avoid data theft 
• Consider generating synthetic data for model training with Gen AI 
• Consolidate data and break silos before implementing AI 

Lack of talent 

In 2022, recruiting and retaining AI specialists was the number one challenge for financial services organizations. In 2023, it slid down to the second most pressing challenge, with 32% of organizations still struggling with it. 

Mitigation strategies: 

• Build a strong employer value proposition 
• Assess candidates’ potential alongside their current technical skills 
• Align your business needs with skill requirements adequately 
• Expand your search to remote and global talent pools 
• Consider sourcing AI talent via partnerships with service vendors 

Legacy burdens 

Traditional banks and insurance companies have notoriously old legacy IT estate, with a median age reaching over a decade in both cases. Adopting AI tools may prove challenging or even impossible without addressing technical debt and modernizing the legacy IT estate first. 

Mitigation strategies: 

• Conduct a digital maturity assessment 
• Reduce IT estate complexity 
• Avoid underestimating scope, time, and budget during legacy modernization 
• Break down organizational silos along with software and data ones 

What is the future of AI in finance?