Why AI for Finance?
Deploying AI in financial services not only has the potential to drive operational efficiency but also creates more opportunities to better understand and interact with customers.
For example, AI technologies such as machine learning and deep learning allow businesses to automatically recognize patterns in transactions to help detect fraud or respond to market trends. NLP drives large language models (LLMs) to enable AI-powered chatbots and personal assistants that interact with customers and professionals, helping answer questions and deepen our understanding of client needs and potential solutions.
Overall, these innovations help empower FSI businesses to be more competitive and adaptive while helping them meet strict regulatory and compliance requirements.
Retrieval-Augmented Generation (RAG) for Financial Services
As financial services institutions evaluate the business potential of LLMs and generative AI (GenAI), which uses AI to generate content, RAG can help reduce liability resulting from inaccurate responses. RAG is an innovative approach to LLM deployment in which the AI model references an enterprise-specific knowledge base when responding to queries. As a result, AI-generated responses can be more knowledgeable about enterprise-specific product offerings, branding, and business requirements. Since financial services operate within strict regulatory environments, RAG can also help automate and improve the accuracy of compliance reporting.
Benefits of AI in Finance
Deploying AI in financial services offers numerous benefits, including extending the capabilities of workers, offering more- personalized services and interactions with customers, and automating back-office processes to potentially help save time and reduce operating expenses. These benefits could have a massive impact on global economies according to J.P. Morgan Research, which estimates that GenAI could add up to US$10T, or 10 percent, of value to global gross domestic product (GDP).1
As financial institutions fulfill their digital transformation goals, AI is not only a cornerstone but also a means to help meet these goals in a more compliant manner. AI’s ability to analyze vast quantities of data in near-real time also supports decision-making and helps automate the detection and prevention of fraudulent transactions or even helps organizations detect and respond to cybersecurity threats that elevate risk for regulated industries.
J.P. Morgan Research estimates that generative AI could add up to US$10 trillion to global GDP.1
AI Use Cases in Finance
Many use cases are already demonstrating the value of AI in financial services, with many more innovations on the horizon. Here are just a few examples:
- Anti-money laundering (AML) and fraud detection: AI can analyze transaction patterns in near-real time to identify suspicious activity and alert financial institutions for prompt investigation and remediation.
- Digital currencies and crypto markets: AI-enabled monitoring and analysis extend to digital currencies to help ensure the integrity of transactions. AI-driven predictive analytics can also help forecast market trends, aiding investors in making informed decisions.
- Code generation: Enterprise IT departments at financial institutions are using AI personal assistants to help developers write code for new projects, enabling fast experimentation and making it easier to support other departments.
- Personalized financial advice and financial product offerings: AI personal assistants are now sophisticated enough to analyze customer information and risk profiles to help manage asset portfolios and offer financial direction or products, making information easy to access.
- Confidential computing and federated learning: Financial institutions can deploy proprietary AI models to analyze and learn from shared pools of encrypted customer data from other organizations while maintaining the confidentiality of their intellectual property and their client relationships. This results in more-refined AI models that are better at recognizing patterns and trends.
- Credit risk assessment, qualification, and know your customer (KYC) processes: AI can analyze vast amounts of information, including combined bank records, to help organizations minimize risk and liability.
- Liquidity and risk management: AI can significantly increase the speed of market analysis and risk calculation for trading positions in securities, commodities, foreign currencies, and other investments while complying with international regulations, such as the Fundamental Review of the Trading Book (FRTB) standards.
- Capital markets trading, high-frequency trading (HFT): AI plays a crucial role in powering automated trading systems that execute transactions at optimal times based on proprietary strategies and market conditions in environments where milliseconds can impact rates of success. Several of these deployments also rely on AI to help ensure the integrity and confidentiality of the models used and the data being processed.
- Analyzing unstructured data: In support of several of the use cases already mentioned, AI and NLP have also become more effective at gathering insights from vast amounts of unstructured data, such as social media and news, to help gauge market sentiment and predict future trends.
The Future of AI in Finance
In the present state of the industry, AI in financial services already has the potential to drive advancements in efficiency, personalization, and security. As AI technologies evolve, more-sophisticated AI models will likely be able to provide even deeper insights and more-accurate predictions. LLMs and GenAI-driven personal assistants will potentially be able to engage customers with greater empathy and the ability to understand customer intentions, leading to greater personalization. AI’s role in risk management and fraud detection will become more robust, keeping pace with digital threats and acting on real-time data to secure transactions and protect assets.