This article is by Pritesh Tiwari ,Founder Chief Data Scientist, Data Science Wizards
Artificial intelligence is getting a lot of investment in banking, financial services and insurance. Every week it seems like there’s another AI tool being introduced, like virtual assistants, document processing systems models to detect fraud or chatbots to help with customer service all promising to change how things are done.
Yet despite this abundance of AI tools, many financial institutions are finding that genuine enterprise-wide transformation remains frustratingly out of reach. The reason is surprisingly simple. The challenge is no longer a lack of AI capabilities. It is the absence of an architecture that allows those capabilities to work together securely.
For BFSI organisations, the next phase of AI adoption will not be determined by how many AI models they deploy, but by how effectively they orchestrate them.
The Growing Problem of AI Fragmentation
Over the past two years, many organisations have pursued AI through individual use cases. One team deploys an underwriting assistant. Another introduces AI for claims summarisation. Customer service implements a chatbot. Compliance experiments with document analysis, while fraud teams build separate machine learning models.
Each initiative may deliver measurable value independently.
Collectively, however, they often create another layer of operational complexity.
Different models operate with different security policies. Business teams manage separate prompt libraries. Knowledge sources become duplicated. Governance varies between departments. Audit trails become fragmented. Integration with core banking and insurance platforms becomes increasingly difficult.
Building a smart company
Organisations often end up with separate intelligence systems that don’t work well together. This means AI spreads across departments but not usually across the whole business process from start to finish. Insurance and banking work through steps not just talks. There’s a misconception that using AI to transform a company starts with conversations. In reality insurance and banking organisations work through a series of steps.
A person checking insurance claims doesn’t just do one thing. They look up the persons policy examine documents make sure the policy covers the claim check claims look for signs of fraud follow rules talk to the customer and handle exceptions.
Loan approvals work in a way. The process involves checking the customers details verifying documents assessing credit checking policies analysing risks using pricing models and reviewing compliance before making a decision. These aren’t problems with AI. They’re problems, with getting everything to work together smoothly. Being successful depends on bringing many systems, data, AI tools, business rules and human approvals in a safe and trackable way. These are not isolated AI problems.
They are orchestration problems.
Success depends on getting systems and data sources to work together with special AI agents and business rules and it also needs human approvals to happen in a safe and trackable way. The decision making process is very important. Adding another AI assistant does not usually solve this problem.
Why Security Must Be Built Into Orchestration
When we talk about security we often think about keeping the AI models safe.
In places like banks and financial institutions the big challenge is to keep the whole decision making process safe. Every time an AI system is used it might deal with information about customers like their financial records or personal details or confidential business information.
As AI becomes a part of how companies work they need to think about some very important things, like security and how to keep customer information safe.
Which model processed the customer’s information?
What enterprise systems were accessed?
Which documents influenced the recommendation?
Why did the AI reach that conclusion?
Who approved the final outcome?
Without clear answers, organisations face significant operational and regulatory risk. This is why secure orchestration matters.
Rather than treating governance as a separate compliance exercise, orchestration embeds security directly into workflow execution through controlled access, policy enforcement, auditability, and role-based decision making. Trust becomes an architectural capability rather than a procedural afterthought.
AI Should Coordinate Work, Not Replace Judgement
There is increasing discussion around autonomous AI agents performing complex business activities.
For regulated industries, autonomy should not be confused with independence.
Insurance underwriting, claims settlement, lending decisions, anti-money laundering investigations, and financial advice continue to rely heavily on professional judgement. The best AI systems know that people are important. AI systems should help experienced professionals do their jobs better by taking care of tasks and giving them the right information when they need it. For example think about someone making an insurance claim. They send in a lot of papers like photographs estimates for repairs letters from the customer medical reports, old policies and signs that someone might be trying to cheat.
A set up AI system can look at all these papers find the important information check if the policy covers the claim find any mistakes look at old claims and make a plan that is easy to understand. The person who works with insurance claims is still the one who makes the decision. The difference is that they can use their skills to make judgments instead of just doing paperwork. This is very important because the people who make rules want to know more, about how AI systems help make decisions.
The people who make rules want to know that AI systems are making decisions. This is something that will become more important as time goes on and AI systems are used more and more to help make decisions like insurance claims decisions.
Model Choice Will Continue to Change
Another misconception is that selecting the right Large Language Model represents the most important strategic decision.
History suggests otherwise.
The AI landscape is evolving too rapidly for organisations to build their operating model around a single provider.
Some models excel at reasoning.
Others specialise in multilingual communication, document understanding, code generation, or cost-efficient inference.
Tomorrow’s leading model may not be today’s market leader. Successful organisations therefore separate business workflows from individual models. By introducing an orchestration layer between enterprise processes and AI services, institutions gain the flexibility to adopt new models without redesigning operational workflows each time the market evolves.
This approach also supports hybrid environments where proprietary, open-source, and internally hosted models coexist according to regulatory, security, or performance requirements.
Observability Is Becoming as Important as Intelligence
As AI systems become embedded within critical financial operations, organisations need visibility into how those systems behave over time.
It is no longer sufficient to know whether a model generated an answer. Leaders increasingly want to understand response quality, latency, model utilisation, knowledge sources accessed, workflow bottlenecks, user adoption, and decision outcomes.
Operational observability has become essential for improving AI performance while satisfying governance and regulatory obligations.
Without it, scaling AI becomes difficult because organisations cannot confidently explain or optimise how intelligence flows across the enterprise.
The Next Competitive Advantage
The financial services industry has historically competed through better products, stronger customer relationships, and operational excellence.
AI will certainly influence each of these areas.
However, competitive differentiation is unlikely to come from possessing another chatbot or deploying another language model. Those capabilities are getting easier for everyone to use.
Organisations that can securely link data across areas like underwriting, claims, customer service and internal operations will have an advantage. This requires bringing everything smoothly rather than just collecting more and more data. The future of AI in banking financial services and insurance will be more about how different AI systems work together securely are managed consistently and fit into current business processes.
The institutions that understand this change, on will move past just testing AI in small projects and build something much more useful: a company where data and insights are shared securely across all workflows, decisions and customer interactions.
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