The integration of Artificial Intelligence (AI) is fundamentally altering the landscape of financial services. According to the State of Application Strategy report: Financial Services edition, over 80% of organizations within this sector have already integrated AI into their systems. This extensive adoption is not surprising given AI's potential. When asked how valuable implementing specific AI use cases would be for their institution, respondents from the Google Cloud Gen AI Benchmarking Study selected 72% or greater (either extremely valuable or fairly valuable) for the following use cases: improved virtual assistants (80%), financial document searches (78%), personalized recommendations (76%), and capital market analysis (72%).
This article will delve into the top AI use cases in financial services, the role of critical AI technologies like retrieval-augmented generation (RAG), and how utilizing the right solutions can help address some of the most serious challenges associated with AI in financial services.
Top financial services use cases for AI
- Enhanced account holder experiences: AI can significantly enrich the user experience through streamlined retrieval of relevant information and better contextual account holder personalization. For example, AI can instantly pull customer history and wallet share during a live call for a call center agent. It also has the potential to direct chatbots and virtual assistants to deliver the most relevant and helpful information, including responses that most likely will result in upselling or cross selling based on the scenario. See how Morgan Stanley is using AI to improve their employee and customer experience here.
- Fraud detection: AI improves fraud detection rates, while limiting false positives, through real-time monitoring and anomaly detection. Look for AI infused solutions that that can adapt faster than criminals can retool, and are empowered by machine learning algorithms based on behavioral analytics.
- Risk management and compliance: AI has the potential to be instrumental in risk management and help organizations ensure they are in compliance with the latest regulatory requirements. For example, by leveraging advanced AI models, organizations can predict market trends and assess creditworthiness with higher accuracy, all while maintaining compliance.
- Operational efficiency: AI drives operational efficiency by automating routine tasks while also ensuring compliance. According to MIT Technology Review, the implementation of generative AI could lead to up to $340 billion in annual cost savings across the financial services industry. Organizations leveraging AI report an 18% boost in customer satisfaction, productivity, and market share, with an average return of $3.50 for every dollar spent.
The role of critical AI technologies like retrieval-augmented generation (RAG) in financial services
As financial services organizations increasingly adopt AI to enhance customer experience, improve fraud detection, streamline risk management, and boost operational efficiency, leveraging the right advanced AI techniques and technologies is crucial. One such technique is retrieval-augmented generation (RAG).
RAG combines the strengths of information retrieval and natural language generation to produce more accurate and contextually relevant outputs. In essence it takes the deep intellectual property or private data from enterprises and combines it with the power of generative AI models, like OpenAI’s ChatGPT. It works by retrieving relevant documents or pieces of information from multiple databases, which often are in distributed environments, and uses them to quickly generate coherent responses.
In financial services, RAG plays a pivotal role in enhancing various AI-driven use cases. For instance, from our previously mentioned user experience AI example in customer service, RAG improves response accuracy and context. A customer service chatbot using RAG can pull information from internal enterprise sources like account details and transaction history to provide precise and personalized responses, leading to better customer experiences.
Additionally, RAG can help streamline operations and ensure compliance with regulatory requirements by automating the retrieval and processing of more relevant documents and data.
Unfortunately, with all the benefits of RAG, it also comes with challenges that commonly stem from relying on workloads that span disparate infrastructure technologies and environments.
Addressing RAG challenges with the right solutions for AI
Key challenges associated with RAG in financial services include performance lag, data security risks, and potentially being out of compliance. These challenges can significantly impact a financial services organization’s operations and AI potential if not addressed properly. Partnering with the right solution provider, can help address these challenges effectively:
- Poor traffic management and performance
- Solution—load balancing: RAG processes involve heavy data retrieval and generation tasks, which can lead to high traffic volumes. It would be ideal to have a load balancing solution that efficiently manages traffic distribution across servers and distributed environments, ensuring optimal performance and minimizing latency, thus maintaining the responsiveness of AI systems.
- Sensitive data exposure
- Solution—application security: RAG systems handle sensitive financial and personal data, making them prime targets for cyberattacks. An API security solution or an AI Gateway product can protect against threats and vulnerabilities, safeguarding the integrity and confidentiality of the data used in AI processes.
- Not meeting regulatory compliance and subsequent fines and remediation
- Solution—better compliance and reporting capabilities: Financial services institutions must adhere to stringent regulatory requirements. Having a logging and analytics solution that can capture detailed logs of AI activities, including data access, processing, and output generation can be crucial in helping financial services streamline their compliance audit process. This level of detail is crucial for demonstrating compliance with regulatory standards, as it provides a clear audit trail of AI operations. The most advanced solutions in the market now have AI infused into their logging and analytics capabilities to help summarize logs.
Looking forward: Minimizing AI challenges and setting up for success
AI's significance in the financial services sector cannot be overstated. It has the potential to revolutionize customer experiences, enhance fraud detection, improve risk management, and streamline operational efficiency and compliance. The role of critical AI technologies like retrieval-augmented generation (RAG) in augmenting these capabilities is a big part of this potential but come with new challenges to consider. Having the right solutions in place can play a pivotal role in addressing the challenges associated with RAG. Learn more about why modern AI apps require the most modern solutions here.
