Financial services firms have the opportunity to fundamentally change the way they serve customers using data. Banks, credit unions, capital markets and lenders have huge amounts of data from various different sources that can be leveraged for better risk management, greater profit margins, stronger customer relationships, and to prevent fraud and financial crimes.
To capitalize on these opportunities, financial services firms are hiring data scientists and investing in new technologies at a rampant pace, but translating data into real solutions that drive business value require more. The Ayasdi Platform provides financial services firms with a new approach to analyze and find insights, anomalies or similarities between sets of large and high-dimensional data without queries or requiring data scientists to write lines of code.
Fraud is an expensive problem to detect and prevent spanning from money laundering, to credit card fraud, to wire transactions and organized fraud rings. Analyzing highly nuanced across large and varied data sets, such as financial transactions, video sensor data, website click patterns, and location information is key to predict fraud patterns and prioritize future prevention.
Optimizing returns, minimizing loan defaults, and managing credit risk can prevent massive losses and improve growth strategies for client portfolios. Risk management best practices includes gathering data from different lines of business and across disparate sources, and integrating this data into credit scoring models and forecasting tools for decision making.
To drive customer loyalty and acquire new customers, financial service firms have created highly diversified product and service offerings. The ability to match data from disparate data sources to understand what products and services customer’s have, as well as, combine this data with household, transactional and demographic information provides financial services firms with the edge needed to maximize customer relationships and profit margins.
Today’s third-party credit scoring models have their limitations and lack the data needed to create an accurate picture of all facets that comprise a credit score. Leveraging financial data such as payment history, assets, debt, and credit types with real-time third-party data can allow financial institution to build a state-of-the-art credit scorecard across millions of customers.