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 will drive revenue growth through personalised products, smarter customer engagement, and faster innovation. Generative AI, systems that can create new content in response to prompts, can analyse vast data sets to detect threats and ensure compliance. Firms can test offerings quickly while at the same time 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.
how AI is used in finance, and what’s next?
The financial industry is entering a new era where artificial intelligence is set to transform how customers bank, invest, and manage their finances.
more personalised services
Banking, investment, and insurance experiences cater to individual customer needs and behaviours.
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 minimised. Automated tools are increasingly learning to handle complex queries and tasks in real time.
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.
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.
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.
ML enhances financial crime prevention by identifying patterns, making predictions, and solving complex problems.
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.
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 AI in finance?
The path to successful generative AI implementation begins with a sharp focus on a strategic roadmap, practical use cases, and a clear vision. By aligning generative AI strategy with business goals, prioritising risk mitigation, and empowering the workforce, enterprises can stay ahead of the curve and build a competitive edge.
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.
