Financial Analytics
Use financial and customer data to identify trends, opportunities, risks and the drivers behind portfolio performance.
India · USA · UK
Turning financial data into business intelligence
Edulyt helps banks and financial institutions transform complex data into actionable insights, predictive models and better business decisions, on problems involving risk, customer behaviour, segmentation, prediction and revenue.
About Edulyt
Analytics built from real financial-industry experience
- Established
- 2015
- Experience
- 11+ years in financial analytics
- Team
- 40+ analytics professionals
- Markets
- India, United States, United Kingdom
Edulyt started its journey in 2015 and has grown continuously over the last eleven years. We work with financial institutions across India, the United States and the United Kingdom, solving complex business problems through analytics.
Our team is around 40 analytics and data science professionals working on sophisticated financial and customer analytics projects. They have spent their careers close to portfolios, not just close to data.
Understand the business problem.
Analyze the data.
Build the right solution.
Create measurable business value.
Our analytics expertise
Analytics that solves real business problems
Eight capabilities, each built around a decision a financial institution has to make.
Risk Analytics
Build analytical solutions that measure and manage risk at customer and portfolio level.
Predictive Analytics
Apply statistical and machine-learning techniques to historical data to anticipate customer behaviour and business outcomes.
Customer Analytics
Understand customer behaviour, characteristics, financial profiles and how those patterns shift over time.
Customer Segmentation
Group customers by behaviour, risk profile, financial characteristics and business potential.
Customer Profiling
Develop comprehensive profiles that give decision-makers a clear view of who they are lending to and selling to.
Credit Analytics
Support credit assessment, customer scoring and default prediction with models built for the decision they serve.
Revenue Analytics
Find the opportunities that increase customer value: retention, cross-sell and revenue growth.
Our analytics solutions
From data to decisions
Edulyt combines business understanding, financial-domain knowledge and advanced analytics to turn complex datasets into practical solutions. The value is created at the end of the chain, not the start.
Data
Raw, fragmented, unmodelled
Analytics
Structured and interrogated
Insights
Patterns that hold up
Prediction
Modelled forward
Decision
Policy and action
Business impact
Measured in the portfolio
Worked example
One credit decision, modelled
A lender has to choose where to draw its approval line. Move the cutoff and the trade-off becomes explicit: volume against expected loss, priced rather than argued.
This is the shape of the work. The model is the means; the decision it supports is the deliverable.
Retail credit portfolio
100,000 customers, scored 300 to 900
300450600750900
49.1%
Approved
49,076 of 100,000 customers
2.64%
Expected default rate
In line with current policy
ApprovedApproved, high riskDeclinedCurrent policy 660
What these solutions let a financial institution do
Every engagement ends in something the business can act on: a score, a segment, a ranked list, a policy recommendation, a monitored model.
- Understand customer behaviour
- Identify high-risk customers
- Predict customer defaults
- Score customers on risk
- Segment customer populations
- Identify revenue opportunities
- Improve customer retention
- Predict future business outcomes
- Support data-driven decision making
Technology is the enabler. Analytics is the outcome.
Edulyt uses advanced technologies to build analytics solutions designed around real business problems. The stack is chosen by the problem, never the other way round.
“We don’t use technology for technology’s sake. We use it to solve business problems through analytics.”
- Artificial Intelligence
- Pattern discovery at scale
- Machine Learning
- Prediction where relationships are non-linear
- Data Science
- Experiment design and validation
- Python
- Modelling and automation
- SAS
- Regulated, production-grade analytics
- SQL
- Data engineering and extraction
- Statistical Modelling
- Explainable, defensible models
- Predictive Modelling
- Scoring and forecasting
How we solve complex analytics problems
Four steps, in order. Skipping the first one is why most analytics work fails to land.
01
Understand
Start with the business objective, the financial environment and the decision the client actually needs to make.
02
Analyze
Explore the data, identify patterns, examine customer behaviour and separate signal from noise.
03
Model
Develop statistical, predictive and machine-learning models where they earn their place.
04
Deliver
Convert analytics into insights and solutions the business can act on and monitor.
Examples of problems we solve
Analytics for complex financial problems
A sample of the work. Each one starts as a business question and ends as something a team can run against a portfolio.
Default prediction
Analyse customer behaviour and financial characteristics to estimate the probability that a customer will default.
- Typical inputs
- Repayment history, utilisation, bureau attributes, transactions
- What we deliver
- Probability of default per customer, ranked by decile
Our team
40+ analytics professionals. One focus: business impact.
Edulyt is powered by around 40 analytics and data science professionals working on complex financial and customer analytics problems.
What the team combines
Financial domain knowledgeAnalyticsData ScienceMachine LearningStatistical ModellingPredictive AnalyticsProgrammingBusiness Intelligence
We don’t just analyze data. We understand the business problem behind the data.
Our partners
The people who own the work
Every engagement is led by a partner who stays on it, rather than passed down after the pitch.
John Doe
Partner, Risk & Credit Analytics
Scorecards, default prediction and credit policy for retail portfolios.
John Doe
Partner, Customer Analytics
Segmentation, profiling and retention across banking relationships.
John Doe
Partner, Data Science
Predictive modelling, machine learning and model governance.
Our industry experience
Eleven years, three markets
Since 2015 Edulyt has kept working the same problem set, which is why the experience compounds rather than resets.
- 2015
- Founded
- 11+
- Years of industry experience
- 40+
- Analytics professionals
- 3
- Markets served
Analytics work from year one
Continuous, compounding
Consultants, not generalists
India / USA / UK
Corporate responsibility
Creating the next generation of analytics professionals
Edulyt gives selected college students the chance to work on structured projects derived from the analytics problems our consultants meet in the field.
We don’t just teach students analytics. We show them how analytics is used to solve real business problems.
Students gain exposure to
- Real-world datasets
- Business problem statements
- Customer Analytics
- Financial Analytics
- Data Analytics
- Predictive Analytics
- Machine Learning
- Python
- SAS
- SQL
- Data Science
From learning analytics to applying analytics
Many students learn technologies one at a time. Our approach shows them how those technologies come together on a real business problem, which is the part an interview panel or a first employer actually asks about.
Technology skillsAnalytics knowledgeReal-world projectsBusiness problem solvingIndustry-ready talent
Helps students prepare for
- Analytics interviews
- Technical interviews
- Campus placements
- Internships
- Data and analytics roles
- Industry projects
Two ways to work with Edulyt
Consulting engagements, and academic partnerships
For financial institutions
Solve complex business problems through analytics
Consulting engagements built around the decision you need to make, delivered by analysts who understand the portfolio behind the data.
- Financial Analytics
- Risk Analytics
- Customer Analytics
- Predictive Analytics
- Data Analytics
- Machine Learning
- Statistical Modelling
- Revenue Analytics
For universities & students
Bring real-world analytics into the classroom
Structured exposure to the kind of analytics problems our consultants meet in the field, adapted for students.
- Real-world datasets
- Industry problem statements
- Analytics projects
- Financial analytics
- Predictive analytics
- AI & ML
- Industry mentorship
Why organizations choose Edulyt
Financial domain understanding
We know the analytics problems financial institutions face because we have spent eleven years inside them.
Real-world analytics experience
Our expertise comes from solving practical business problems, not from running exercises on clean data.
Strong technical capability
Analytics combined with AI, machine learning, Python, SAS, SQL and statistical modelling.
Experienced analytics team
Around 40 professionals working on complex financial and customer analytics problems.
Business-focused approach
The objective is not a report or a model. It is an insight a decision-maker can act on.
Industry-to-academia bridge
We use our industry experience to create meaningful analytics exposure for the next generation.
Have a complex analytics problem?
Let us turn your data into actionable business intelligence. Whether you are a financial institution with a problem to solve or a university looking to give students exposure to real-world analytics, Edulyt can help.
info@edulyt.com / India · United States · United Kingdom