Delivering financial services products requires the coordination of many different processes, capabilities, and organizations.
When you consider the detailed attributes of the customer, the product, the delivery channel, and the regulatory jurisdiction for each transaction or interaction, there are almost infinite potential configuration permutations.
Managers must add resources and processes to understand and evaluate performance, risk, and compliance. Regulators’ continuously increasing demands exacerbate this problem.
How do I know if the controls I’ve implemented are working?
Are they appropriate for the risks I am looking to manage?
Are they effective in mitigating these risks?
Are they efficient? Does the realized benefit exceed the cost?
A control may seem appropriate at the most granular level, but it may be redundant or superfluous when evaluated against an adjacent control. It could be overkill because the likelihood or severity of the risk is so small, or it could be wholly insufficient.
Banks have inventories of controls and processes to evaluate them. But, as the volume of information grows, the ability to analyze this information with traditional means is overwhelmed. Any manually driven process to rationalize a complex control infrastructure will take so long that the business will change during the effort, severely limiting its impact.
How can AI and data help?
Advanced data management techniques can create a virtualized representation, a “Digital Twin” of the business, including its processes, products, and channels. These techniques, including graph databases, natural language processing, semantic analysis, predictive analytics, and large language models, can help banks better understand the business’s true performance and the corresponding management controls.
Graph databases allow you to better model the interdependencies across an organization and align it to a standardized business ontology. As more data is inserted into the graph, machine learning algorithms can detect patterns humans can’t.
Natural language processing can read document text and use semantic analysis to align the document (policy, procedure, org chart, etc.) to the right part of the ontology and extract its meaning and purpose.
Predictive models can then generate and populate the key performance and risk indicators that measure the business’s health. These models can also simulate the impact on the KPIs and KRIs emanating from proposed business or operating model changes.
Lastly, attaching an LLM supports natural language interactions with your operating model configuration, significantly improving the quality and productivity of the measurement and analysis process.
The combined impact of these technologies creates a truly fact-based representation of your control infrastructure and the ability to rationalize them.
