Customer trust and loyalty

Embrace promising technologies to put a stop to financial crimes

Minimize The Cost Of Fraud While Strengthening Customer Trust And Loyalty

From the largest global organizations to the smallest companies, financial crime has become an increasing concern for all. Moreover, when it comes to fraud, it is not only limited to monetary losses, but its impact extends to reputation, customer trust and loyalty, business relations, shareholder confidence and the bottom line. That’s why detecting and preventing financial crime has become essential for all eCommerce merchants, financial services firms and small/mid digital lenders.

Secure banking is not an unrealistic goal now

Although banks are a favorite target for hackers, they are also among the most sophisticated enterprises in the world from a security perspective. Banks have begun to embrace promising technologies like machine learning, predictive analytics and other artificial intelligence techniques to defend themselves against potential breaches and data loss proactively.

Financial companies can now respond to fraudsters’ techniques as they appear using our data intelligence, as it is aimed at monitoring, analyzing, learning and predicting human behavior. Data intelligence can detect even the smallest change in shopping behavior of customers which means that they can track and assess the risk. Not only does data intelligence becomes smarter with every transaction as the process is fully automated, but it can also detect new trends with the help of outlier detection and network analysis.

Whether they make a financial transaction using a mobile phone, IoT device, internal network, a website, an app, or payment cards; at every potential access point, they are adding more protection to the database themselves that holds the key to the information the criminals are after. The use of innovative technologies to combat fraud is now a worldwide phenomenon.

There are three main ways in which data intelligence looks for and detects frauds:

  • Searching for a precise and well-defined pattern of activity on the system that contradicts the financial institution’s system and identifies breaches of the firm’s controls.
  • Carrying out analysis to detect abnormal behavior that may not in itself breach and approve it as the ‘normal’ patterns of new user activity.
  • Carrying out an analysis on a particular account or system user’s activities over an extended period, handling bodies of data from multiple systems that could run to tens of billions of transactions – particularly useful to find out if an internal employee is involved in financial crime.

Talk to us about your data complexities and let data intelligence address them