The growth in customer data and online activity has made criminal endeavors even more complex to deal with than they were in the past. Loans frauds are expanding to multi-transactional frauds that can involve hundreds of people, and detecting these transactions is now more difficult than ever, since they are often disguised within a backdrop of genuine transactions.
Companies that can proactively respond to large-scale fraud operations to detect them faster or, in the best case, prevent them, can save millions of dollars every year in losses. But despite being awash in huge amounts of data from multiple sources, many organizations are unable to extract enough intelligence to allow them to act proactively. They face two major barriors:
Organizational Silos: With data trapped across multiple systems, financial services organizations are not able to consolidate their data in a single repository and in unified datasets for effective analysis, pattern matching, and risk modelling.
Privacy, Security, and Governance Restrictions: To avoid the risk of data breach, organizations refrain from releasing sensitive information for analysis, foregoing intelligence that would allow them to take pro-active measures.
The PHEMI Central Big Data Warehouse allows financial institutions to:
- Handle any volume and variety of data, and ingest and consolidate data from multiple disparate sources.
- Take advantage of built-in privacy, security, and governance controls, including the ability to de-identify data, so sensitive data can be shared with the right people at the right time. Customer privacy is protected but access to appropriate forms of data is provided to those who need to know.
- Produce incremental or real-time aggregations of data, as well as timelines with sub-second response times.
- Rapidly process fraud and risk reports against all other datasets in real time.
Now, with transparent views and access to all of their information, financial organizations can be pro-active in detecting fraudulent activity across all of their data, allowing execution of system-wide prevention strategies.