Making GenAI Work in Banking: Ontologies, FIBO, and Knowledge Graphs
Generative AI (GenAI) is all the buzz, with everyone talking about how it will revolutionize the way we work.
But how much of this is just hype, and how much is reality?
It’s comparable to the introduction of computers 70 years ago. Back then, every banking process was manual, and today, everything is digitized across the enterprise. Looking back, the transformation was truly revolutionary—but it didn’t happen overnight. It took many phases of transition, implementation, and—importantly—cleaning up data and processes.
Like any technological shift, GenAI requires some groundwork before it can deliver real value. The challenge lies in the vast amounts of data that banks have accumulated over the years. Much of it is stuck in silos, stored in inconsistent formats, and difficult to access. As AI and automation reshape the financial industry, managing, standardizing, and interconnecting data is critical for success.
This is where ontologies, the Financial Industry Business Ontology (FIBO), and knowledge graphs come in. These tools empower banks to improve data governance, enhance decision-making, and unlock AI’s full potential. For banking executives, understanding and adopting these technologies is no longer optional—it's essential.
Why All the Hype?
Banks deal with a wide range of data, from customer profiles and financial transactions to regulatory filings. This data comes from different sources, often stored in legacy systems that struggle to communicate with one another. The result? Data inconsistencies, manual reconciliations, and inefficiencies that slow down critical decision-making.
Data standardization and organization through ontologies and knowledge graphs are critical as banks endeavor to leverage AI technologies. Ontologies and knowledge graphs allow banks to integrate disparate data sources, standardize definitions, and ensure that all systems speak the same language.
Banks that harness data to leverage GenAI applications will have a competitive edge, enhancing customer service, improving compliance and risk management, and driving operational efficiencies.
What Are Ontologies and Knowledge Graphs?
An ontology is a structured framework that defines and categorizes terminology and data. It shows properties, potential values, and relationships among data entities. In simpler terms, it provides a blueprint to organize information.
While an ontology is the blueprint for how data is defined and structured, a knowledge graph is the real-world application that uses this structure to connect data and discover insights.
A knowledge graph is a network of relationships between data points, which allows banks to query complex relationships in real time. It links customer profiles, transaction histories, regulatory requirements, and more into a cohesive, searchable framework.
One of the most significant benefits of knowledge graphs is their ability to break down data silos. In traditional systems, data from different departments—such as customer service, compliance, and risk management—often remains disconnected. Knowledge graphs, using an ontology framework, integrate this data, turbo-charging contextual insights.
The integration of data from varied sources enhances decision-making by providing real-time insights into customer behavior, risk exposure, and operational performance. For example, by linking transaction data with external sources like market data, banks can detect unusual patterns and identify potential fraud earlier than before.
What Is FIBO
Developing an ontology is no small task. There are hundreds, if not thousands, of data fields across various systems, not to mention external sources, such as market data. Each of those fields needs to be defined, mapped, and connected with relationships.
Fortunately, the EDM (Enterprise Data Management) Council created an industry standard for the financial services industry. FIBO (Financial Industry Business Ontology) is a specific ontology developed to bring clarity and consistency to financial data.
FIBO acts as a shared vocabulary for financial terms, allowing banks to define complex concepts in a precise, standardized way. It enhances regulatory reporting and improves auditability while creating alignment across departments and reducing operational errors. Standardized financial terms make data analytics more accurate and easier to query across systems.
By adopting FIBO, banks can quickly adopt knowledge graphs and GenAI with a future-proof, adaptable data structure. The industry standard enables banks to deploy AI applications and reap the benefits of AI in record time.
The Strategic Advantage
The financial landscape is becoming increasingly data-driven and competitive. The institutions that harness their data effectively in AI applications will lead the way.
Ontologies like FIBO and knowledge graphs are no longer futuristic technologies—they are essential tools for enabling transformations such as:
- Virtual Assistants: AI is revolutionizing customer service with virtual assistants that are far more advanced than today’s chatbots. These agents will handle inquiries and transactions 24/7 with human-like accuracy and superior efficiency, offering personalized advice and support as technology advances.
- Hyper-Personalization: By using GenAI, banks can tailor products to individual customer needs, boosting satisfaction and loyalty. By analyzing vast amounts of data and trends, such as transaction history and financial goals, GenAI can generate clever personalized recommendations quicker than ever.
- Regulatory Compliance: GenAI can help banks automate compliance activities, ensure faster and more accurate responses to regulatory changes, and reduce the risk of non-compliance.
- Fraud Detection and Risk Management: Models will analyze real-time transaction data to detect unusual patterns earlier than humanly possible. It will also create synthetic data to simulate fraud scenarios, allowing banks to test and refine detection systems.
- Operational Efficiency: Automating routine tasks, like document processing and report generation, will reduce manual workloads and allow staff to focus on strategic initiatives, leading to cost savings and productivity gains.
Make GenAI Work in Your Bank
As the financial services industry increasingly adopts GenAI applications, bank executives must focus on simplifying and streamlining data management. Ontologies, FIBO, and knowledge graphs provide powerful tools for standardizing, integrating, and enriching financial data, ultimately driving operational efficiency, regulatory compliance, and customer satisfaction.
Now is the time for banks to adopt these advanced data tools to stay ahead in an increasingly data-centric world and secure their position in the financial landscape of the future.
DataKite.ai supports banks by streamlining data management and implementing AI-powered technologies. Contact us to see how Datakite.ai can help make GenAI work for your bank.