Fraud analytics professional
As the world witnesses a surge in fraud incidents, leveraging artificial intelligence (AI) for fraud detection could be the key to saving millions of dollars in financial fraud losses. Organizations worldwide are increasing their investment in AI-based fraud detection solutions, indicating that the industry is bullish on the capabilities of AI in fraud.
The question arises as to why AI draws attention with respect to fraud detection now. There are two reasons:
- Technological advancements in recent years have facilitated development in computational abilities; and
- The availability of a variety of nontraditional data.
Firstly, while the core concepts of AI were developed more than 25 years ago, they were not practically implementable because AI techniques like deep learning and machine learning models require huge computational power to run. This hurdle has been overcome, as we have made tremendous progress in democratizing and implementing these complex AI-based models.
Secondly, companies have enormous amounts of data needing to be analyzed. Data is the new fuel, the depth and breadth of which, if correctly analyzed, can give multidimensional insights. AI-based algorithms can be used to analyze massive amounts of structured and unstructured data to find fraudulent patterns and anomalies in scenarios where traditional rule-based or score-based approaches fail.
For instance, traditional analytical models may not be able to connect links that fraudsters leave behind in the vast ocean of data. However, AI has the capability to churn large amounts of data, connect the pieces and complete the puzzle to produce a clear picture of fraud threats. Also, these models self-learn and improve with time.
Traditional approaches for development of fraud detection rules involved analysis of transactions data, payments data and other customer data. However, with the advent of AI, we can analyze new social media, audio and video data. Streams of video recorded at bank branches, ATM terminals or other public places can be analyzed along with traditional data by employing AI algorithms. This analysis, in turn, provides strong indicators not only to detect fraud but to predict fraud even before it happens.
That time is not far when more organizations will move toward AI models built on varied and new data sources. This will be the next battlefield where the new generation war will be fought, and it's a battle we should be prepared to fight.
Vipul Srivastava works in the fraud risk department of a global bank and has keen interest in exploring new ways to mitigate fraud risk. The views expressed in this article are solely those of the author in his individual capacity.