The Planning details are received by the system from employee, using which expense report is generated, while AI engine is looking for any exceptions. Planning details include Beat Plan, Business Trips, and Official Travel. Employee validates the report post which the AI engine, once again looks for any deviations such as – duplicate claims, policy exceptions, geofencing exceptions, overstated expenses, personal expenses or fictitious claims. In addition to looking at each individual claim in question, machine learning algorithms recognize patterns in data looking at – historical claims and customer visits, trends of expenses for employees in similar roles, etc. to flag off suspicious claims. Other elements of the user profile, such as notice period, percentage of round value expenses, nature of claims, role, etc. are also used as inputs to the AI model.