Elute Insights

Financial Data Analytics, Machine Learning & AI Consulting

Helping banks, fintechs, payments companies, and financial services teams improve profitability, optimize pricing, make better customer decisions, strengthen forecasting, and manage risk.

Financial Analytics • Predictive Modeling • Forecasting • Customer Analytics • Pricing Analytics • Risk Analytics • Experiment Design • Causal Study

Kanak Agrawal

Financial Data Science & Analytics Consultant

With over 8 years of data science and analytics experience across PayPal, Capital One, and the broader financial services sector, I combine deep domain expertise with advanced data science, machine learning, and predictive modeling. I partner with financial institutions to mitigate risk, optimize pricing, improve customer retention, and unlock measurable revenue growth.

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Services we offer

Three areas of work, each scoped to a measurable business outcome.

Advanced Data Analytics

We analyze customer, product, financial, and operational data to identify revenue opportunities, uncover performance issues, understand customer behavior, and support better strategic decisions.

Customer analytics Product analytics Financial analytics

Predictive Modeling & Machine Learning

We build predictive models that help businesses forecast demand, predict churn, score risk, segment customers, and prioritize high-value opportunities.

Forecasting Risk scoring Churn prediction

AI & Analytics Automation

We build AI-powered tools and workflows that automate repetitive analytical work, extract information from documents, generate insights, and support faster business decisions.

AI workflows Analytics automation Dashboarding

Watch Our Work

Watch live demonstrations and walkthroughs of selected data analytics, machine learning, and AI projects to see how we approach real business problems from end to end.

Visit our YouTube channel

Schedule a call

Let’s understand your problem and identify the best path forward.

Discuss your business challenge, data, and goals with us. We’ll help clarify the problem, identify potential analytical approaches, and determine how data, machine learning, or AI could help.

Book a consult

30 min

Introductory consultation

Engagement Models

Three ways to engage, depending on scope and how long you need support.

Hourly

Best for flexible or short-term support when the scope may evolve. Work is billed only for time used, with clear time tracking and regular invoicing.

Start small and scale as needed.
Begin with a small number of hours, set a spending cap, and pause or stop at any time before committing further.

Fixed project-based

Best when the scope, timeline, and expected outcome can be agreed upfront. We define the project in advance, set clear milestones, and agree on a fixed fee before work begins.

Start small and scale as needed.
Larger projects can be broken into smaller phases, with clear deliverables and milestone-based progress, so you can review each stage before committing to the next.

Monthly retainer

Best for businesses that need ongoing access to data science, machine learning, or AI expertise. A fixed monthly fee provides an agreed level of support and predictable monthly costs.

Start small and scale as needed.
Begin with a short initial engagement, review the value and level of support regularly, and continue or expand month to month as your needs evolve.

Projects

Real-world projects across analytics, machine learning, and business intelligence

Problem

A global payments company needed to understand the business impact of merchant pricing changes across a major Southeast Asian market.

Solution

Led end-to-end analytics for merchant repricing, combining behavioral and financial KPI monitoring with causal inference techniques to measure the impact of pricing changes on transaction volume and revenue.

Outcome

Identified and supported pricing decisions that drove $5M+ in incremental payment volume.

Problem

A global payments company needed to measure the true impact of a pricing change on transaction volume and revenue, but there was no suitable control group for a conventional experiment.

Solution

Built a Synthetic Control causal inference framework to construct a counterfactual benchmark from comparable markets and quantify what business performance would likely have looked like without the pricing intervention.

Outcome

Enabled the business to isolate the incremental impact of the pricing change on payment volume and revenue, supporting more rigorous pricing decisions.

Problem

A large financial institution needed better visibility into how customer credit risk was changing across its credit card portfolio.

Solution

Designed a credit-risk monitoring framework and dashboards tracking key KPIs including delinquency, charge-offs, recoveries, and portfolio performance, supported by monthly and quarterly analytical reviews.

Outcome

Enabled leadership to identify emerging risk trends faster and make more informed credit and portfolio strategy decisions.

Kanak Agrawal

Financial Data Science & Analytics Consultant