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Solving Prominent Challenges with AI in Financial Services

Written by: Chad Davis, Sr. Industry Solutions Marketing Manager

The financial services sector is undergoing a profound transformation due to the incorporation of artificial intelligence (AI). According to the F5 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 to 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 explore the leading use cases of AI in financial services, the significance of essential AI technologies such as retrieval-augmented generation (RAG), and how implementing the appropriate solutions can tackle some of the major challenges linked with AI in the financial sector.

Leading AI use cases in the financial services industry

  • Improved 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 information during a live call with 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 $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 importance of key AI technologies such as RAG in the financial services sector

As institutions 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 RAG.

RAG combines the strengths of information retrieval and natural language generation to produce more accurate and contextually relevant outputs. In essence, it takes 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.

With AI in financial services, RAG plays a pivotal role in enhancing various AI-driven use cases. For instance, in our previous example of how AI can enhance the account holder experience, RAG improves response accuracy and context. A customer service chatbot using RAG can pull information from internal enterprise sources, such as 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, despite its benefits, RAG also comes with challenges that commonly stem from relying on workloads that span disparate infrastructure technologies and environments.

Overcoming RAG challenges with effective AI solutions

Key challenges associated with RAG in financial services include performance lag, data security risks, and the potential of 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 by:

  • Minimizing performance lag with improved 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.
  • Reducing sensitive data exposure with enhanced 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.
  • Streamlining regulatory compliance and reducing subsequent fines and remediation with improved compliance and reporting capabilities: Financial services institutions must adhere to stringent regulatory requirements. 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.

Mitigating AI risk and setting up for success

AI's impact on financial services cannot be overstated. It has the potential to revolutionize account holder experiences, enhance fraud detection, improve risk management, and streamline operational efficiency and compliance. The role of critical AI technologies like RAG in augmenting these capabilities is a major part of this potential but comes with new challenges to consider.

Having the appropriate 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.