From manufacturing to logistics, Artificial Intelligence (AI) is playing a transformative role across industries by optimising operations. The financial sector is no exception. With Machine Learning (ML) algorithms automating processes, analysing data and improving decision-making, AI has redefined traditional finance, ushering in an era of precision, efficiency and speed.
Benefits of AI in finance and accounting
The benefits of AI in finance and accounting are manifold. The first and foremost is automation, wherein AI bots take over repetitive tasks from humans, enhancing accuracy and ensuring faster completion. By reducing manual intervention, AI-powered automation improves service delivery and decision-making.
AI’s advantages also extend to the domain of risk management where ML tools are being increasingly used to better outcomes. These algorithms have the capacity to analyse vast volumes of data to identify patterns and evaluate creditworthiness, which improves profit margins and reduces loan defaults. Enhanced compliance is an added benefit of AI, which can be leveraged to track and analyse transactions, identify potential risks and ensure financial activities comply with regulatory standards.
The role of AI in financial decision making
AI supports financial decision making by analysing large volumes of financial and operational data in real time and surfacing insights that finance teams would otherwise take days to compile, turning routine reporting into a faster, more data driven decision making process.
Integration of the technology is also empowering businesses and finance professionals, who are widely using AI for decision making. AI algorithms analyse huge data and provide quick insights to facilitate faster and wiser decisions.
Customer experience
Customer experience is another key area where AI’s deployment is proving revolutionary. From automated payments to money transfer, the technology has been an enabler, ensuring faster service delivery and improving customer satisfaction. Gone are the days when one had to wait endlessly for a service executive to resolve their issues. AI-based chatbots and virtual assistants are available 24/7 to provide instant responses. This apart, finance professionals can leverage AI to offer personalised financial products and services to customers.
As opposed to traditional methods, AI tools can analyse much larger datasets much quicker, facilitating more comprehensive financial analysis and fostering innovation in products and services,
Besides these, AI offers scores of other benefits. No surprise then that it’s being adopted by finance professionals worldwide. A Gartner survey of financial leaders revealed that AI in finance has increased significantly with 58% using the technology in 2024.
Real-world use cases of AI in financial decision making
AI in finance is being extensively employed for tasks like credit scoring and risk assessment. Here are some use cases businesses are putting to work in AI in financial decision making for businesses of every size.
Investment advice and financial planning
With the help of sophisticated algorithms, AI bots offer affordable and personalised investment advice based on investors’ goals, risk tolerance and market conditions, thus optimizing financial planning.
Fraud detection and risk mitigation
Deploying AI also significantly enhances detection of anomalies like fraudulent transactions, financial crimes, spoofing in trading and cyber threats, giving a shot in arm to security, risk-mitigation, anti-money laundering (AML), Know Your Customer (KYC) and compliance initiatives.
Credit scoring and creditworthiness
Additionally, intelligent bots can analyse massive volumes of data and extract valuable insights, which facilitate a deeper understanding of market trends and prudent decision-making. Based on the analysis of data points like social media behaviour and other online activities, AI determines the creditworthiness of customers. With AI algorithms automating tasks like credit scoring and document verification and predicting loan risks, loan processing becomes easier for both lenders and borrowers.
Personal financial management
AI has also altered the manner in which individuals envision their long-term financial goals, aiding better money management. It helps them analyse their spending patterns and risk tolerance, apart from recommending customised budgeting advice and saving strategies.
Not just investors, even financial service providers benefit from AI bots’ in-depth analysis, which enables them to assess and mitigate risks effectively. This fosters a more stable and secure financial environment.
Portfolio management and predictive analytics
Portfolio management and predictive analytics are two other vital use cases of AI in finance. The former involves the usage of advanced analytics through which portfolio managers can gain sharp insights into economic indicators and market trends, making more informed decisions regarding investor goals.
When it comes to predictive analytics, ML algorithms can make predictions by identifying patterns in data, helping finance professionals anticipate customer behaviour.
Customer engagement and personalisation
AI has also revolutionised how financial service providers engage with customers by predicting their behaviour and purchase patterns. This facilitates personalised interactions, faster and more accurate customer support, credit scoring refinements and innovative products/services.
Generative AI in financial decision making
AI continues to evolve and bring forth new disruptive technologies that can further enhance financial functions. One of them is Generative AI, which can create content in text, audio and video formats based on human prompts. Statistics show it’s being widely adopted across sectors: the global generative AI in financial services market was valued at USD 1.95 billion in 2025 and is projected to reach USD 17.88 billion by 2035, growing at a CAGR of 24.81%, according to Precedence Research.
Generative AI tools are being used in diverse applications, including risk management, trading-related algorithms, fraud detection, customer services automation, forecasting of financial data and more.
With the continuous advent in technology, AI is no longer an option but a business imperative across sectors. Deploying an AI strategy enhances the efficiency of operational processes, drives personalised customer experience, facilitates reliable service recommendations and builds trust, thereby helping businesses stay ahead of the competition.
How can Infosys BPM help?
Infosys BPM offers finance and accounting outsourcing services for clients worldwide. We leverage cutting-edge technologies to provide customised digital solutions to our clients. We help them maximise efficiency and effectiveness while providing a better customer experience.
Frequently asked questions
AI in financial decision making carries real risks alongside its benefits: model bias in credit and risk scoring, over-reliance on automated outputs without human review, data privacy exposure when AI systems process sensitive financial information, and regulatory compliance gaps as rules around AI-driven decisions continue to evolve. Effective governance, human oversight, and regular model audits help manage these risks.
AI supports liquidity decision-making by continuously analysing cash flow patterns, payment timing, and market conditions to forecast short-term funding needs more accurately than periodic manual reviews. This lets treasury and finance teams act on real-time signals, such as an emerging cash shortfall or an unused credit facility, rather than discovering the issue at the next reporting cycle.
AI decision support in finance refers to systems that analyse financial data and present recommendations or risk flags to a human decision-maker, rather than acting fully autonomously. It sits between basic reporting tools and fully automated decisioning, and is the layer most of the use cases above (credit scoring, fraud detection, portfolio management) actually run on today.
Agentic AI is moving financial decision making beyond single-task automation toward AI systems that can carry out multi-step workflows with minimal human input, such as monitoring transactions, flagging anomalies, and triggering a compliance review automatically. This builds on the generative AI applications already in use for forecasting and fraud detection, rather than replacing them.
