Consumer Analytics and Engagement
Engaging your customer at the right time with the right offering through the right channel is what defines the engagement needs of today. Consumers are generating an estimated 2.5 exabytes of data every day, through different channels, internal and external, which finance companies must tap into for achieving marketing effectiveness.
With disparate sources, such as CRM, ERPs, Social Media feeds, Web Data, with data either structured or unstructured and real-stream, BIRD proves to the most effective analytics platform.
Some Use-Cases that BIRD can address
- Customer Segmentation
- Tracking campaigns
- Understanding customer attrition
- Discover key influencers on your customer transactional behaviors
- Identify key topics of interest for your Customer
- Sentiment analysis
Advanced Custom Solutions: We use advanced ML based solutions to predict Customer Life time Value, Customer Attrition, Profitability Modeling
Real-Time Fraud Detection
There is no question on the seriousness and the absolute need of having this functionality in financial institutions. Frauds happens in various aspects of an institution such as identity thefts, system hacking, data theft etc. It is especially important that the detection of these threats happen in real-time.
Using the descriptive and advanced ML models, we can identify the patterns of Model customer behavior on large amounts of data. Millions of customer interactions, transactions, system logs should be analyzed in real-time to detect any out of the ordinary behavior happening with the data using the learnt Model behavior.
BIRD’s lambda architecture where the real-stream data can be fused with batch data is most helpful to not leave any details that are useful for pattern detection.
Risk management is mandatory for financial institutions, to maintain the trust and regulatory requirements.
Risks can come from different aspects, such as competitors, investors, regulators, or company’s customers. Also, risks can differ in importance and potential losses. With BIRD, you can identify, prioritize, and monitor risks. Our advanced ML models can be used to train on the huge amounts of customer data, financial lending, and insurance results to automate and enhance the risk scoring models, thus improving efficiencies.
Credit Risk Assessment is one of the most important applications of Risk Management and at BIRD, we offer a solution to automatically identify the credit worthiness of a customer based on data coming from his past payment behavior, credit history, net value etc.
There are many other use-cases such as P&L statement analysis, revenue growth identifiers and key influencers that BIRD lets the users easily slice and dice data, down to the row level, which is most important for financial institutions