A stock portfolio gets automatically rebalanced based on market signals. A loan application is approved or rejected within seconds after a risk model evaluates the borrower’s creditworthiness. These actions reflect the pivotal role of AI in finance. Once reliant on manual processes and conventional data analysis, the financial sector is quietly leveraging AI to improve efficiency, reduce costs, and lay the groundwork for long-term growth. Financial businesses that have already deployed automated workflows and streamlined operations are seeing measurable gains. This article explores the role of AI in finance, how AI is used in finance, and what’s next, including machine learning and use cases.
the role of AI in finance
Beyond cost savings, over 70% of financial services executives expect AI in finance will drive revenue growth through personalised products, smarter customer engagement, and faster innovation. Generative AI systems, a key advancement in AI financial services, can analyze vast data sets to detect threats and ensure compliance. Firms can test offerings quickly while simultaneously strengthening risk management and cybersecurity.
AI also plays a key role in reaching underserved segments of society for people without credit histories, especially in developing regions. AI helps to create financial profiles, giving unreached segments access to loans and other financial products. This is opening new opportunities for economic participation and growth.
the evolution of AI in financial services
The journey of AI in finance began with basic rule-based systems but has evolved dramatically over the past decade. Traditional financial services relied heavily on human judgment and standardized processes, creating inefficiencies and limitations in scale. The evolution of AI in finance has transformed this landscape through progressive innovations:
- Early adoption focused on automating repetitive tasks
- Mid-stage development introduced predictive analytics and pattern recognition
- Current implementations leverage sophisticated machine learning models and natural language processing
how AI is used in finance, and what’s next?
The financial industry is entering a new era where artificial intelligence in finance is set to transform how customers bank, invest, and manage their finances. With global AI in finance market projections reaching $190.33 billion by 2030 (according to Markets & Markets), financial institutions are racing to implement solutions that enhance customer experiences while improving operational efficiency.
Banking, investment, and insurance experiences cater to individual customer needs and behaviors through AI-driven personalization. AI financial services enable institutions to analyze customer data and provide tailored recommendations, product offerings, and financial guidance. This transformation has resulted in:
- Enhanced customer satisfaction through individualized service delivery
- Increased product adoption rates through targeted recommendations
smarter automation
Digital platforms, powered by AI, streamline workflows, improve operational efficiency, and reduce manual effort.
faster, data-driven support
AI-generated insights and automated decision-making tools deliver quicker and more relevant advice for customers.
AI-powered chatbots and virtual agents
The need for human customer service intervention is minimized due to the effective use of AI tools. Automated AI tools are increasingly learning to handle complex queries and tasks in real time, enhancing efficiency in the financial services industry.
enhanced decision-making with new technologies
Accuracy and responsiveness are enhanced with the emergence of small language models (SLMs), retrieval-augmented generation (RAG), and AI agents.
trading and Investment Applications
Machine learning in finance has transformed trading and investment strategies through:
- Algorithmic trading that processes 10,000 data points per second
- Portfolio optimization that balances risk and returns more effectively
- Market sentiment analysis that incorporates news, social media, and economic indicators
quantum computing integration
Quantum computing is enabling financial firms to analyse massive datasets faster, improving fraud detection and risk management.
strategic AI alignment
By integrating AI investments into broader business strategies, financial institutes can balance innovation, cost control, and time-to-market.
regulatory compliance and security
As AI in finance becomes more prevalent, regulatory compliance and security have emerged as critical focus areas. Financial institutions must navigate:
- AML and KYC requirements through automated monitoring systems
- Data privacy regulations across multiple jurisdictions
- Ethical considerations in algorithmic decision-making
AI financial services now incorporate multiple security layers, including anomaly detection, behavioral biometrics, and continuous authentication. However, they also introduce new risks like adversarial attacks and data poisoning. The most secure implementations use “security by design” principles, with regulatory frameworks like the EU AI Act establishing specific standards for AI in finance.
how is machine learning used in finance?
Machine learning (ML), a subset of AI, plays a vital role in transaction monitoring by analysing large volumes of data to uncover patterns in market behaviour. This enables predictive analytics for trading and investment decisions.
Financial institutions leverage machine learning algorithms to process unstructured data from multiple sources, identifying correlations and insights that would be impossible for human analysts to discover.
Explainable AI (XAI) promotes transparency, especially in areas like credit scoring and lending, by helping us understand and justify ML-driven decisions.
Robo-advisors, powered by ML, offer personalised investment advice to individual goals and risk profiles, democratising access to sophisticated financial planning.
the numbers speak: scale and impact
- Banks could see an extra $170 billion or 9% in profits by 2028 via AI, with global banking profits poised to reach nearly $2 trillion by 2028.
- Around 93% of financial firms believe AI boosts profits, despite half of bank roles being automation susceptible.
- Generative AI alone could generate $200–340 billion annually for banks by 2030 showcasing the potential of AI applications in the financial services industry.
use cases of AI in finance
Significant use cases in finance include:
- A regional bank used generative AI to boost developer productivity by 40%, with over 80% of participants reporting improved coding experiences during a proof-of-concept aimed at accelerating software delivery.
- A large bank is leveraging AI enterprise-wide to enhance customer and employee experiences, improve efficiency, and drive profitability, using it for personalised financial guidance and to proactively manage loan risks in small business banking.
- UK financial firms are adopting AI to boost efficiency, improve risk management, and enhance services, particularly in fraud detection, customer support, and credit risk, while focusing on strong governance, data quality, and responsible model use.
how can Infosys BPM help you harness the power of (artificial intelligence) AI in finance?
The path to successful generative AI implementation begins with a sharp focus on a strategic roadmap, practical use cases, and clear vision. By aligning generative AI strategy with business goals, prioritizing risk mitigation, and empowering the workforce, enterprises can stay ahead of the curve and build a competitive edge. Infosys BPM specializes in helping financial institutions navigate the complexities of AI implementation, from initial strategy development to full-scale deployment and optimization.
A successful generative AI strategy for business goes far beyond technical capabilities – it demands forward-looking leadership and smart execution. Businesses that act with purpose and foresight can seize this opportunity and shape the new era of value and innovation, using AI technologies.
FAQ
While AI in finance is automating certain tasks, it’s primarily transforming roles rather than eliminating them completely. According to recent research, 43% of finance tasks could be automated, but this creates new roles focused on AI oversight, strategy, and customer experience design. Financial professionals who upskill in AI literacy will find expanded opportunities in this evolving landscape.
The best AI financial services depend on specific applications. For investment analysis, platforms like FinBERT offer specialized NLP models. For fraud detection, solutions like Feedzai provide 99.5% accuracy rates. Enterprise solutions from IBM, Google Cloud, and Microsoft offer comprehensive AI stacks customizable for various financial applications.
The role of AI in finance will continue to expand with more autonomous, transparent, and personalized systems. By 2027, we expect to see AI agents executing complex financial strategies, explainable AI providing clear rationales for lending decisions, and hyper-personalized financial advice based on holistic consumer data.
AI-powered risk management systems incorporate multiple security layers, including anomaly detection and behavioral biometrics. However, they also introduce new risks like adversarial attacks. The most secure implementations use “security by design” principles and human oversight of critical decisions, with regulatory frameworks establishing specific security standards for AI in finance.
