Fintech

Case Studies

Menerva Software’s customized fintech analytics solutions offer insights that help financial institutions and banks to monitor and manage customer attrition and retention trends; recognize patterns of fraudulent transactions, predict next such fraud and take preventive action; measure branch profitability and viability.

Use Cases for Fintech

Customer Segmentation

Target customers and micro-segments to drive increases in traffic, sales, profit and retention

Customer Lifetime Value Forecast

Know your customer’s future purchasing behavior and the profit associated with each of them

Manage Customer Churn

Monitor and manage customer attrition and retention trends

Cross-sell/Up-sell

Discover and take advantage of cross-sell/up-sell opportunities

Fraud Analytics

Recognize patterns of fraudulent transactions, predict next such fraud and take preventive action

Branch Efficiency Analysis

Measure branch profitability and viability set sales targets, accordingly

Competitive Analysis

Use competitive analysis to formulate a go-to-market strategy for organizational growth

Common Industry Challenges

Business Goal - Maximize Profits

#1#1

Focus - Happy Customer

Key Business Questions

  • Can the customers who are most satisfied and profitable be identified? They usually have high CLTV [Customer Lifetime Value] scores.
  • What are their attributes, the driving factors behind their portfolios?
  • What can the bank do to increase the share of this customer base?
  • Can the bank segment the customers in this base even further to achieve better profitability and minimize customer churn?
  • How can the bank leverage existing as well as external data to improve security and proactively protect its customers from fraud?

Problems Addressed by Data

  • Acquisition and retention of profitable customers, over an extended period.

Explore how our data management solutions can help you acquire and retain more customers.

#2#2

Focus - Profitable Products

Key Business Questions

  • Which products offered by the bank are most profitable?
  • What is the optimal correlation between a given customer segment and product set that generates the most revenue? If so, are there opportunities to up-sell or cross-sell to customers in those segments who could benefit from these product sets?
  • Are there opportunities to discontinue or suspend low-performing products?

Problems Addressed by Data

  • Identify the most revenue generating products for the bank.
  • Identify the products most likely to sell and to which segment.
  • Measure product propensity.

Explore how our data analysis & adhoc querying solutions can help you identify your most profitable products, happiest customers and up-sell/cross-sell opportunities.

#3#3

Focus - Profitable Branches

Key Business Questions

  • Can all banks & financial institutions with branches know which of their branches is performing well by product, customer churn, attrition and acquisition?
  • Can employee actions be tracked so that high performance can be incentivized?
  • Can using third-party or external datasets from social media and publicly available FDIC data give the bank valuable insight on public’s perception of the bank and impact how it deals with customers, affecting customer acquisition and churn.

Problems Addressed by Data

  • Identifies branches that are making the most revenue and profit for the bank.
  • Provides insights on how to improve the profitability of branches.
  • Points out low performing branches, which are leaking revenue to be shut down.

Explore how our data processing & insights solutions can help you improve the performance and profitability of your branches.

Case Studies

Automating algorithms for block trades improving accuracy and productivity

A registered investment advisory firm with AUM of $140 million was spending 8 hrs to 24 hrs for processing block trades. This case-study explains how Menerva automated the process to bring the time down to under 30 mins.
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Insights related to stock price drop to make stock purchase decisions

A registered investment advisory firm with AUM of $140 million was spending about 4 hrs on stock analysis. This case study explains how Menerva helped them bring the analysis time down to less than 2 secs.
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Visualizing a model’s output and customizing it for each client

A financial services company had a proprietary model to assess and compute a performance score for each of their business clients manually. This case study discusses how Menerva automated the process such that the score was computed simultaneously for all clients.
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