Mohan Kumar

Process Mining : Lead the development of KPIs to identify process gaps, deviations and potential red flags for different segments in the Home Loan Application process. ~ Helped client improve user experience and fix bottlenecks in the process increasing the operational efficiency by 10%. Technologies used : Power BI , SQL, MS Excel

Customer Segmentation Model for Banking Profiles: Developed a customer Categorization Model to create customer segments based on their transactional behavior, demographics, geographic location, product usage, etc. ~ Helped flag possible illegal activities and combat money laundering. Functionalities used : Feature Selection, Feature Engineering, PCA, k-Medoids

Anti Money Laundering Solution Development : (a)Implemented Anti Money Laundering(AML) Transaction Monitoring (TM) solutions across various Banking Products like Current Account Savings Account (CASA), Credit Card (CC) using tools like Mantas.

(b)Performed User Verification Test to understand the data and perform data cleaning.

(c)Developed and Tuned several Risk Indicator scenarios to find out possible red flags by monitoring the transactions of the customers using ML and Statistical Model

(d) Involved in ATL ( Above Threshold Line) and BTL (Below Threshold Line) activities to test the threshold generated

(e) Creating MIS daily and monthly projections and presenting key insights to the key stakeholders. tools like and analytical tools ~ Helped client identify potential Money Laundering transaction for 10 countries and Also, reduced the false positive by 80%. Skills : Python, SQL, Hive, Data Science, Project Planning, Data Analysis, Transaction Monitoring, Anti Money Laundering, Tableau

Bill Fraud Detection : Creation of model for identifying the fraud Wifi and Telephone bills submitted by associates for Reimbursement claims. ~ Helped identify the associates following non ethical ways and saved money for client

Credit Risk and Scorecard Model Development: Identify and predict the Expected loss as per standard-IRB using Probability of Default and assigned credit score to existing and potential borrowers. ~ Found the probability of a customer of paying back the loan amount so that the bank can achieve maximum profit and avoid losses reducing the company's overall credit risk by 10% through strategic risk mitigation tactics . Python libraries used : Pandas, Numpy, Scikitlearn, Statsmodel

Topic Modelling : Creation of model for classifying various Industry levels of Ball Bearing to ensure proper inventory data management ~ Helped decrease logistic costs, maintenance costs, inventory costs which led to a reduction in order processing time by 7% . Python Libraries used : Pandas, Numpy, re, Scikitlearn, gensium

Reliability Analytics : Created model for predicting expected number of cars for repair and the associated cost using feature engineering and Random Forest algorithm. Python Libraries used : Pandas, Numpy, re, Scikitlearn

Vendor Selection Optimisation Problem : Creation of equations for cost, quality and service optimization and determine vendors providing products at maximum benefit by considering the factors like per unit cost, quality and service ~ Helped in cost, quality and service optimization. Python Libraries used : Pulp, Pandas, Numpy, Tkinter

Design Churn Analysis : Performed Data Preprocessing and automation using python and prepared Power BI dashboard to determine the current amount of non RFT releases per program

~ Helped rectify non RFT and approx.10% improvement in RFT resulting in cost saving

Automation of Unauthorised User Access Determination : Performed automation of external application user list to convert to IDM specific format using Python which saved company's time and reduce risk. ~ Helped analysis and audit of user's access to designate access to users, evaluate risks and revoke from users having unauthorized access. Python libraries used : Pandas, Numpy