How AI Helps Banks Detect Fraud and Measure Risks

Artificial intelligence or AI helps banks detect automating fraud, cybersecurity, and anti-money laundering. According to EMERJ, approximately 26% (the largest share) of the venture funding raised for AI in the banking industry is for fraud and cybersecurity applications.

Here we give you a low-down on the different AI approaches banks can employ to detect frauds related to payments, loans, and customer onboarding.

AI-driven real-time fraud solutions

Banks and financial institutions struggle to find the right technology to analyze transactions and detect suspicious activities in real-time.

Artificial intelligence, machine learning, and big data are changing the face of fraud detection by bringing real-time solutions. Machine learning uses advanced analytics to collect data about incoming transactions. Meanwhile, AI can adapt to produce touch techniques and deliver actionable insights in real-time.

Anomaly detection

Online payment fraud losses will exceed $200 billion in the next five years. Banks and financial institutions are vulnerable to illicit activities, making their detection a growing necessity. Here we share five ways AI and machine learning can help banks identify and prevent fraud.

1. Behavioral analytics

It uses machine learning algorithms to anticipate and understand each account holder’s transaction behavior (spending and saving) patterns. From this, the program can identify any behavioral anomalies. Algorithms treat uncharacteristic spending as suspicious behavior.

2. Large data sets

Machine learning (ML) improves accuracy with an enormous amount of data from big data analytics. Millions, even billions of data points, enable a smart computer to assess whether a transaction is fraudulent.

Encounter with as many examples as possible is necessary for AI to be useful. Best in class models continually learn from additional data to adjust their decisions based on changing environments in real-time.

3. Supervised and unsupervised machine learning

Supervised models have previous input and output variables for the machines to learn from the past and predict future events. It tags the transaction as fraud or non-fraud to let computers determine legitimate or illegitimate patterns.

Meanwhile, unsupervised models use unclassified and unlabeled data as a form of self-learning. The machines must classify the data without prior knowledge.

4. Predictive analytics

AI models can develop predictive analytics software to assess data with a supervised or pre-trained ML-based algorithm. ML models can use data related to an account holder’s transaction patterns to determine their legitimacy.

Banks can use predictive analytics to prevent activity from incorrectly flagged transactions. MasterCard uses predictive analytics powered by ML for real-time analytics.

5. Fraud detection

It is AI’s most significant contribution to the financial sector. Big data algorithms can easily detect data inconsistencies and discrepancies and ensure fraud prevention. Likewise, AI algorithms are helping protect consumer data and prevent credit card fraud.

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