Data Analytics and AI
in Banking and Financial service
Strategya2AI uses AI, NLP, and machine learning to help banks in fraud detection, risk management, customer sentiment analysis, and personalized marketing.
With technology advancements, financial services have become customer-centric, requiring the ability to analyze large digital data for success.
Our solutions enhance business user productivity and decision-making efficiency across the organization.
Features include but are not limited to:
Risk Management
Risk management in banks has evolved significantly over the last decade as new threats emerge. Regulations have also become stricter in the aftermath of the global financial crisis. As a result, S2AI's use of Data Science and Machine Learning to reinforce risk management practices aided in identifying complex, nonlinear patterns in large volumes of data and creating more accurate models.
Customer Data Analysis
In today's hyperconnected world, valuable data flows incessantly into the financial services industries. Banks, however, collect large amounts of data from consumers in various formats and through various touchpoints due to a lack of appropriate technology and an over-reliance on manual data handling. S2AI analyzes these datasets in a timely manner based on information gathered from social media, customer surveys, and data from other touchpoints to provide banks with a better understanding of customer sentiment. S2AI teams use machine learning and data science technologies to deconstruct these data sets in order to provide deep data intelligence on customers' needs, wants, and perceptions of the bank.
Cash Demand Forecast
Forecasting ATM cash demand is a challenging task. When forecasting results are too high in comparison to actual demand, this results in excess cash at bank ATMs and the cost of lost interest. On the other hand, if the forecast is too low, bank customers will be dissatisfied due to cash-outs. S2AI team developed a deep learning forecasting model that predicts expected Cash Demand with a minimum optimized margin of error.
Customer lifetime value
The ability to assess customer lifetime value (CLV) at the outset of a customer interaction enables businesses to shift from a focus on quarterly profits to a customer relationship management strategy that has already demonstrated increased long-term profitability. By categorizing customers based on their CLV, S2AI enables banks to properly focus their efforts on improving service quality and increasing overall staff productivity.
Fraud detection
Machine learning algorithms enable the timely detection and suppression of fraudulent operations involving bank cards, accounts, and transactions, among other things. S2AI teams developed a machine learning application that can detect new accounts that generate suspiciously expensive purchases. We also put in place systems to monitor unusual transactions based on behavioral profiles. For example, if a customer orders a transaction that is out of character for them, banking algorithms may require additional confirmation to complete it.
Marketing & Sales
The key to marketing success is to tailor an offer to specific customer preferences and needs. By dividing the data into demographical, geographical, and historical data sets, data science in banking can help create a personalized window for each customer. These datasets provide more information about how a customer responds to an offer/promotion. As a result, banks can engage in personalized outreach to customers. Machine learning contributes to the development of powerful recommendation engines that can generate upsell/cross-sell opportunities for banks.