Build Secure, Compliant & Predictive Data Systems for Finance Business

Finance industry & its challenges with data

The fiscal world of cyber risks and market twists

Credit risk

From lending institutions to insurance sectors, risk mitigation is always a meticulous process. Challenges include evaluating creditworthiness, portfolio diversification, regulatory oversights, and economic instabilities are inevitable while doing things manually.

Need to process data in real time

Regulatory compliance

Phishing attacks and finance fraud

Competition from fintechs

End-to-End data analytics solutions

Detecting fraud with data and AI

Fraud detection and analytics models

Use data and analytics to detect fraud in real time with negligible false positive instances, while handling large volumes of transactions with ease. By automating fraud detection, you could save money and processing time, allowing legitimate users to access user-friendly banking and financial services.

We help you design and develop models to detect all types of finance fraud - chargebacks, phishing, identity fraud, fraudulent claims, suspicious transactions, check fraud, and more like this. Let’s help you overcome traditional data management challenges and implement a fraud analytics solution that scales with your data.

CUSTOMER SPOTLIGHT

Success stories from businesses like you

Fraud analytics solution for an NBFC client

Achieved fraud detection rate

Developing a risk mitigation framework to detect and flag fraudulent claims

Read full story

Resolving duplicate beneficiary data for integrated CSR management

8.12%

Duplicates cleaned up using Zingg AI

Beneficiary training cost wastage identified

Developing a platform to automate data cleansing and identity resolution using Zingg.

Read full story

ML-based sales forecasting system predicts loan sales accurately

90%

Forecasting accuracy

Designing a Loan Sales Forecasting System for a Non-banking Financial Institution

Read full story

FAQS

Got questions? We got you covered.

How is data science used in finance?

Data science is used in finance to find and mitigate fraud, predict demand and revenue, and automate complex yet repetitive processes. There are many data science use cases for finance proven effective – risk management, revenue forecasting, etc., useful for both banking and NBFCs.

What is the future of AI in finance?

Future of AI in finance will be all about hyper-personalized customer service, advanced and automated fraud detection, investment management with real-time risk analysis, and AI-powered virtual assistants, and more streamlined banking operations.

What is financial forecasting and why it is important?

Financial forecasting is predicting company’s revenue, cash flow, profits, and other metrics for any period – based on historical data, market trends, and other economic conditions. This is needed to get valuable insights about budgeting, investment, & resource allocation, and stay away from risks.

CLIENT VOICES

Take our customers word for it

We were really impressed with their service orientation and understanding of our business. Showcasing expertise in data engineering, datakulture was successful in implementing the dashboard, allowing the client to track their KPIs and make data-driven decisions. The team's solid understanding of the client's business and their needs was critical to the project's success.

Biswajit Rath

Head of Analytics, Raymond Limited.

I just wanted to say a big thank you for all the support you've given us. Your expertise in Data and Analytics is really helping us make better data-driven decisions. The way you're assisting us with MS Fabric is fantastic, and we're excited about gradually migrating to it to boost collaboration across our organization.

Fisayo Oduyemi

IT Business Analyst, Saluda Medical

They were flexible and adaptive to the client's changing requirements. The team executed a smooth workflow to ensure the project's success. Their flexibility and adaptiveness were notable in the partnership.

Bhargav Raghavendra

Senior IT Director, Cyient

The team at datakulture are inspiring to work with. Their drive for excellence and the values they share from the top down are impressive. The result is top quality work and a partner you are proud to work with.