While AI has yet to become as prevalent in Indian businesses compared to more mature markets like the US, the adoption of AI in India’s financial services has been 68% in FY2024, even overtaking the 60-65% adoption rate in the tech sector. Financial services businesses, such as banking, insurance and investment management, need to understand AI financial models to remain competitive. Not only do these models simplify conventional financial operations, but they offer insights that enable companies to adjust to market changes quickly.
Read on to understand what AI financial models are, how they function, and the advantages they bring to businesses.
What are AI financial models?
AI financial models are computational systems that employ machine learning (ML), deep learning, and data analytics to study financial data, forecast trends, and automate financial tasks.
AI financial models generally proceed in a sequential manner of data gathering, model training, and deployment.
- Data gathering: The journey starts with the acquisition of enormous quantities of financial information, including past market information, economic information, and transactional information. High-quality, heterogeneous data is most suitable for AI models.
- Model training: Machine learning algorithms in this stage analyse data to detect patterns and patterns. Supervised learning models rely on labelled datasets to make predictions on particular outcomes, while unsupervised models find data structures without labels.
- Testing and validation: After training, the AI model is tested using an independent dataset to measure accuracy and performance. After this step is completed, the model gets deployed.
Types of AI financial models
The various types of AI models, together with their use cases, are listed below:
- Predictive models: Predictive models use past information to predict future financial performance. They are used in financial prediction, trend analysis, and investment strategy development. For example, in retail banking, predictive models can predict customer behaviour, allowing banks to customise products and services to customers’ unique needs. The global predictive analytics market for banking is forecasted to grow at a CAGR of 19.42% by 2030, indicating its growing importance in the sector.
- Classification models: Classification models sort data into pre-defined groups and are used widely in fraud detection, tagging transactions as valid or suspicious according to recognised patterns. Credit card issuers, for instance, use classification models to identify fraud by examining patterns of spending and marking anomalies.
- Clustering models: Clustering models cluster data points according to shared attributes, hence useful in customer segmentation and facilitating companies to effectively target their marketing. A firm can apply clustering, for example, to segment policyholders into risk profiles and thus customise insurance products and premiums. Not only does this increase customer satisfaction, but also resource allocation and marketing are maximised.
- Regression models: Regression models are used to describe the association among variables and are applied to predict values like revenue, expense, and investment returns. A regression model, for instance, can analyse the impact of changes in economic measures, e.g., inflation and interest rates, on share prices. Businesses can then make knowledgeable decisions and develop strategies based on the trends they observe.
Including these AI-based financial models enables firms and financial institutions to make better decisions, become more operationally efficient, and gain insights into their customers, thus becoming competitive in the marketplace.
Advantages of AI financial models
AI financial models deliver several benefits to businesses:
- Improved decision-making: AI models give real-time insights that allow businesses to make timely decisions. For example, AI-based models can forecast the performance of financial products in the future, enabling fund managers to rebalance portfolios to reduce risk and increase returns.
- Operational effectiveness: Task automation reduces the load on financial analysts and enhances processes such as loan processing and transaction handling. In particular, JPMorgan’s AI platform has reduced document review time significantly, improving operations.
- Improved accuracy: Artificial intelligence algorithms minimise the incidence of errors brought about by human behaviour through the provision of consistent and fact-based forecasts. Capable of processing extensive volumes of financial information quicker and more accurately compared to conventional procedures, AI refines predictive analysis and decision-making.
- Risk management: These models enable the discovery of potential risks in the early stages, enabling firms to act promptly to prevent them. AI is able to effectively detect possible risks and fraud, augmenting the overall risk management system.
- Cost savings: By automating activities, companies are able to save operational expenses and increase profitability. For instance, banks will be able to save Rs. 7,800 crore in opex by 2028 through the adoption of AI.
Dilemmas of deploying AI financial models
While they aid with efficiency, the usage of AI financial models is accompanied by some concerns as well:
- Data quality: AI models are dependent on high-quality data to perform well. Unreliable or biased data may result in incorrect forecasts.
- Regulatory compliance: AI models must conform to regulations such as the Digital Personal Data Protection Act in India
- Transparency: Most AI models are “black boxes,” with little to no visibility about how they make decisions. This transparency issue can raise ethical and legal concerns, especially in cases when an AI model makes an incorrect decision or one which causes harm to a human being.
- Integration with legacy systems: Companies can experience difficulties integrating AI models into current financial systems, where technical skills and capital are needed.
Conclusion
Through better decision-making and operation optimisation, artificial intelligence financial models can revolutionise companies. They offer insights, simplify processes, and enable businesses to control risks. AI financial models can guide companies towards success, from automating important tasks, including loan issuance at an NBFC, to simplifying the financial management of an online marketplace.
The right implementation of financial models based on AI needs an understanding of the way they operate, attention towards the quality of data, and compliance with regulations. Organisations that implement a well thought out AI strategy have the ability to maximise the performance of financial models and achieve sustainable growth.
