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Banking & Insurance

 

verticals

 

objective

 

solution

 

solution details

 

Banking

 

To parse, clean and enrich data from various sources and convert the data into a standardized format allowing for faster data analysis.

 

Business Intelligence:

Data parsing and enrichment

 

  • Prepared the various standards acceptable for all fields coming in from source systems

  • Developed automated macros to parse, enrich and update data fields from source systems

 

Banking

 

To identify exisiting customers who would have a higher propensity to buy other products.

 

Sales and Marketing:

-Segmentation

-Propensity Modelling

 

  • Identified the processes which had the most complaints

  • Conducted sentiment analysis to identify the priority of complaints

  • Developed a predictive model to prioritize complaints

 

Banking

 

To identify the key processes where most complaints were raised and also identify the degree of sentiments in order to predict if a customer complaint will be raised.

 

Sales and Marketing:

-Segmentation

-Predictive Modelling

-Text Mining

 

  • Identified the processes which had the most complaints

  • Conducted sentiment analysis to identify the priority of complaints

  • Developed a predictive model to prioritize complaints

 

Banking

 

To develop a propensity to default model on credit card applications.

 

Strategy and Operations:

-Segmentation

-Propensity Modelling

 

  • Using credit application and bureau variables a model was built to segment customer applications into various risk levels

 

Banking

 

To develop response models to identify campaign success metrics.

 

Strategy and Operations:

Predictive Modelling

 

  • Segmented the historical customer data using RFM methodology

  • Performed variable and feature selection to optimize model accuracy

  • Developed logistic regression models to identify customers who should be targeted for the campaign

 

Banking

 

To develop a call volume forecasting model to manage call centre operations.

 

Strategy and Operations:

Forecasting Models

 

  • Gathered historical data including factors affecting business variability

  • Decomposed the data to identify trends and seasonality in the data

  • Developed multiple models using holt-winters and SARIMA techniques

 

Banking

 

Identify factors that influence high attrition and poor performance from among the current data/information collected at the time of hiring. Create a filtration matrix with the identified matrix which can then be used for future recruitment drives.

 

Human Resources:

Workforce Modelling

 

  • Using proprietary Redwood algorithms 4 variables were identified which had the highest impact on attrition

  • Employee referral source was identified as a variable of high importance as it negatively impacted performance/attrition. This insight was garnered as a result of very detailed data mining

 

Insurance

 

Estimate market size and opportunity for new product entry. 

 

Sales and Marketing:

-Segmentation

-Market Analysis

 
 

Insurance

 

To provide reports containing critical information for the sales staff and management.

 

Operations:

-Dashboarding

-Automated Reports

 

  • Maintained and automated risk reports

  • Responded to Ad-hoc queries requests from hospitals and insurance companies