This article has been co-written with Stéphane Rey (CIO at Credit Suisse Asset Management), Rémi Sabonnadiere (Generative AI Strategist – CEO @ Effixis) and Arash Sorouchyari (Entrepreneur, Speaker, Strategic Advisor for Fintechs and Banks).
The banking industry relies heavily on data-driven insights to make informed decisions, but gathering and consolidating data can be a slow and difficult process, especially for large financial institutions. Consider The Financial Company, a fictive global wealth manager with billions of assets under management. The firm has grown quickly through multiple acquisitions, resulting in a complex IT landscape with various Investment Books of Records (IBORs) and data repositories.
At The Financial Company, it can be a challenge for business users to find out how much the company is exposed to a specific country or sector. To get this information, they have to request the IT department to create custom queries from several databases and then wait 1-2 business days to receive the answer. This process is time-consuming and not efficient.
One commonly used approach to solve that challenge involves utilizing business intelligence and data visualization tools like Microsoft Power BI. This approach involves the IT department creating a solution tailored to the specific needs of the business user. However, this approach could be more efficient as it is only reactive and not easy to scale. Each new query or use case requires a new customized solution, which often leads to copying more data into an existing data warehouse or creating a new one. BI developers must identify the correct data in various databases, gain access to them, create extraction procedures, and adjust data warehouse structures to receive the data.
Imagine if business users could get real-time answers without depending on the IT department. This is where the paradigm shift occurs – using Generative AI to change the data retrieval process from a query-based model to a prompt-based one.
Moving From Query to Prompt
Generative AI brings an innovative shift by placing a Large Language Model (LLM) powered agent on top of multiple databases, eliminating the need for never-ending and costly database consolidation. This approach requires two key elements:
- Database Crawlers: To gather data from numerous databases, files, and services with different API technologies is a significant challenge. Database Crawlers can help by connecting to multiple databases, reading their schemas, and comprehending them. These Crawlers can function as domain agents that possess knowledge of a particular domain’s data and context. They are aware of the databases and structures within their domain, eliminating the need for model discovery with each request.
- Generative Prompt: The generative prompt helps interpret user requests, generate query codes, and gather data from multiple databases. The consolidated data is then presented to the user. The prompt can seek user assistance if there is any uncertainty in selecting the appropriate data sources and fields.
By leveraging the exceptional text-to-code abilities of Large Language Models as well as their ability to understand very well both human questions and data dictionaries, it creates an intelligent layer capable of answering many requests in a reliable, explainable, and intuitive way. The benefits for an organization are numerous.
1. Instant Access and Enhanced Decision Making
Generative AI offers banks immediate and reliable access to data, thus empowering real-time decision-making. The ability to query data easily and access it in real-time enables banks to rapidly recognize potential risks and opportunities and make informed strategic decisions.
2. Improved Data Completeness and Accuracy
By accessing data from various sources and utilizing intelligent agents, Generative AI ensures databases are complete and accurate. This significantly reduces errors and improves overall data quality, ensuring that decision-making processes are grounded on current and comprehensive information.
3. Bridging the Skills Gap
GenerativeAI eliminates the need for advanced technical skills, as business users can interact with the system using natural language queries. This bridges the skills gap, allowing users to derive the necessary insights independently and fostering a self-sufficient environment.
4. Scalability and Flexibility
Generative AI systems are inherently scalable and flexible. They can adapt to changing business needs and accommodate new use cases effortlessly. Instead of creating individual solutions for each query, the AI system can dynamically handle various requests irrespective of the underlying database management systems and data structures. This adaptability allows banks to remain agile and swiftly respond to new data demands.
5. Cost Reduction
Generative AI removes the necessity for expensive data migration projects by allowing data retrieval from current, dispersed sources. This leads to significant reductions in both time and expenses associated with data consolidation.
Addressing Data Challenges
1. Data Gathering and Data Quality
Generative AI also utilizes data healers to enhance data quality. However, accessing these data sources with crawlers entails challenges such as access rights, filtering data based on user rights, identifying inconsistencies, merging data, and avoiding overloading transactional databases with queries.
By adopting a domain-based agent approach, each domain agent ensures that performance, access rights, and other issues are tackled. The agents are developed by the respective domains and are equipped to provide answers related to their data model across all databases. Moreover, AI doesn’t bypass the need for IT expertise but enables them to create intelligent agents that can autonomously answer future queries.
Additionally, AI can search online sources for relevant data to deal with incomplete databases. For example, by analyzing articles, the AI system can identify companies associated with the oil and gas sector and create an extra column named “Industry_AI_generated”, which can be automatically populated with pertinent values.
2. Minimizing System Overload
In order to avoid system overload, domain agents should use tactics like read-only database instances, setting up local data storage, or utilizing performance-optimized services, particularly if dealing with transactional databases. It is the responsibility of each domain to handle performance concerns effectively.
Banks can benefit from using Generative AI, specifically LLM-powered agents, to retrieve data from multiple databases. Although AI isn’t a complete fix, having agents that are knowledgeable about their specific domain can greatly help alleviate the issues. These agents act as important components in the data retrieval process, as they’re familiar with the context and data of their domain.
It is important to understand that this technology does not replace the need for IT expertise. Rather, it repositions IT to create intelligent agents that can autonomously answer future queries. This approach aligns with the data-mesh strategy and is a transitional phase that helps IT departments focus on long-term strategies for data management and legacy system transformation.
Banks should begin testing this technology to discover its potential as a game changer. By doing so, they can transform into a data-driven company more efficiently than they anticipated. If you are interested in learning more about this approach or running a proof of concept, please contact email@example.com.
We will soon publish an exciting new episode, where we will introduce a cutting-edge prototype powered by Generative AI. Stay tuned!