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Do you know how banks are getting smarter? It is by turning Knowledge as a Competitive Edge : The real game-changer is how they're using everything they know by decoding the customer brain through data by studying their patter 24/7/ 365 days. 1. They are ditching the old for the new Simplify and streamline operations: They are replacing legacy systems with modular, cloud-native architectures which was un-thinkable just a few years ago. Use continuous integration and delivery tools are used to reduce development time and improve agility. In action: Bank of America using Cloud Provider: Private cloud infrastructure. Saved approximately $2 billion 2. Investing big in data and AI They are Building a solid foundation: Banks are building unified data views from their scattered data sources. To connect all the dots which was their biggest pain. Use AI and Gen AI to generate insights, automate decision-making, and to give a personalize customer experiences. Example: Several Autonomous Decision Intelligence Platform is developed by banks to turn fragmented data into strategic assets for fraud detection, risk assessment, and marketing. 3. Shifting focus from maintenance to innovation: Redirecting funds: Tech budgets are being redirected from simple maintenance to things that where it is creating new value. Customer-first focus: The focus has shifted to improving the customer experience, personalizing everything, and getting to market faster. 4. Nurturing the right talent and culture Build a tech-savvy board and leadership team. Gen AI training are provided to the leaders along with it’s application. There is a significant Increase in the proportion of in-house engineers and reduce overhead roles. Foster a technology-first mindset across the organization. Example: Another US Bank Capital One, with it’s 12,000-strong tech team transitioned to a cloud-first model and began selling its own software products. 5. Use AI to Enhance Relationship Management AI isn't just for behind-the-scenes—it's also a powerful tool for customer-facing teams. Smarter advisors: Relationship managers are being armed with AI-generated client summaries, risk profiles, and insights. More meaningful conversations: They are spending more time on strategic, high-value conversations because gen AI solution is providing them with the necessary inputs. The payoff: This is leading to a stronger, more profitable customer relationships and faster decision-making.

A good example is Human Resources. In my organization, it was gathered, we have over 20 years of documentation that is not organized, but if harmonized and standardized, we can build an AI solution that reduces resource burden from the data management team. A standardized database can help create an Agentic AI solution that can help address inquiry management quickly and only escalate if the resource does not exist in the database.

Companies that have legacy software's or legacy systems usually tend to have a huge knowledge bases. The KB might be accumulate based on the releases and the issues/features that have been worked upon. The hardships that we commonly face are due to unstructured KB's 1. Excessive time searching for fragmented/relevant documentation 2. Inconsistent, Incomplete or Outdated knowledge 3. Excessive time taken to go through inefficient content 4. Knowledge Ownership conflicts : mis-leading to incorrect POC's 5. SME's/KME's Resistance to Change Common areas where waste/risk can be identified/introduced : 1. Redundant Work or Re-work : Introducing new bugs while trying to solve existing bugs. 2. Employee Productivity 3. Operational Efficiency 4. Compliance issues 5. High TAT 6. Damaged Reputation 7. Customer dissatisfaction due to inconsistent service delivery We can use the prompt + flow AI solution to create the structured KB's by tagging, recognizing patterns, building ontologies. Advantages may be 1. Accurate Knowledge Creation 2. Knowledge Storing and Retrieving, 3. Knowledge Application 4. Classification based on patterns 5. Automated Knowledge Base Maintenance 6. Content Gap Analysis 7. Semantic Analysis 8. Automatic Content Curation 9. Intelligent Search - mechanism to find relevant content 10. Insights Generation - like finding previous issues, their resolutions 11. Automated Question Answering 12. leverage waste/risk 13. Drive continuous learning 14. Improve adaptation by creating smart knowledge repositories 15. Handle complex information 16. implement predictive analysis and do forecasting 17. Improve operational efficiency by reducing cost and time wastage 18. Improve TAT 19. Improve customer satisfaction through consistent service delivery without any re-work.

With advent of time, business has become more knowledge based. Detailed documentation of the process, flow charts, , drawings, standards, decision trees, QAPs, legal compliances, periodic updates of SOPs have become integral part of efficient operations. Processors/operators need to refer these document every now & then to main required CSAT, targeted revenue, escalations etc. Conventional ways of referring may not be effective when you are quoting a benefit of medical policy to an online caller because it would be time consuming as multiple clicks through contract details would take the agents to desired clauses. If a LLM based model coupled with Semantic technology is enabled here, the agent can use conversational AI to arrive quickly. Chatbots can be used to refer previous similar questions or advanced FAQ models. Similarly decoding of a complex claim history would become easier if SOPs, remark codes, diagnostic codes are connected using Ontario technology. This will not only make the process efficient & simpler but dependency on a SME will become negligible. Learning curve of average agents will get reduced significantly.