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
5%
Achieved fraud detection rate
Developing a risk mitigation framework to detect and flag fraudulent claims
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Resolving duplicate beneficiary data for integrated CSR management
8.12%
Duplicates cleaned up using Zingg AI
7%
Beneficiary training cost wastage identified
Developing a platform to automate data cleansing and identity resolution using Zingg.
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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 storyFAQS
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.
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