Insights to maximize opportunities and minimize risks
Imagine if everyone in the organization could instantly answer data-driven ad hoc questions and relieve the burden on analysts and data scientists. Whether it's portfolio analysis, customer segmentation, or cash flow analysis, Tellius helps financial services companies discover new opportunities, improve service, and differentiate from the competition.
Increase your “risk intelligence” by clearly defining, understanding, and managing their tolerance for and exposure to risk. Gain clearer visibility in managing the many types of risk in such key areas as operations, regulatory compliance, supply chain, finance, ecommerce, and credit. Enable decision makers across the company to measure, quantify, and predict risk, and rely less on intuition to create a consistent methodology steeped in data-driven insights.
Identifying fraudulent activity is a necessity given the growing sophistication of fraudulent methods and the need for financial services firms to maintain consumer confidence in their products and services. Build machine learning models from transactional data of even the largest data sets to baseline and profile user behavior, evaluate incoming transactions in real-time, and prevent losses before they occur.
Gain customer insights that help drive acquisitions, increase customer engagement and loyalty, and improve customer lifetime value. Derive insights that deliver the right offering to the right customer in the right channels. With a single actionable view of customer relationships, leverage predictive analytics to understand customer behavior and discover customer financial patterns from real data.
Empower your team to optimize returns and manage risk for each and every client with comprehensive and easy to use analytics. Measure performance, risk, style, and characteristics for multiple portfolios and asset classes. Understand composition and risk, view metrics, including weights, valuation measures, ratings, and other ratios for your portfolio and benchmarks, and evaluate relative performance using different attribution models.