Artificial Intelligence vs. Financial Frauds

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Fraud has been a major issue for financial institutions since long. As the global transactions have increased, danger has too. Artificial intelligence has significant potential to reduce as financial
automatic detection & machine learning tools becomes more powerful.The speed at which losses
occur in financial frauds takes place makes fraud detection techniques increasingly important.

An additional benefit of a more powerful model of Artificial intelligence is its ability to use
wide data continuously for different customers of their transactions for accurate comparison.
Thus, as life, circumstances & spending habits of customer changes, model would automatically adjust what it views as fraudulent transactions and so exercise control on such transactions and minimize frauds.

Financial systems are interlinked with Buyers,Sellers,Service providers, Banks & criminals
attack the weakest link to infiltrate systems and make fraudulent purchases and claims.
Fraudsters' attack have evolved using distributed networking, big data and dark web to detect vulnerabilities. They mimic good customers' behavior to game the system. The problem with traditional protection methods is that they are slow learning and can't keep up with changing
fraud patterns. And they have trouble moving the speed of transactions which occurs in seconds.

Organisations need powerful solutions that react in real-time & can learn patterns quickly to recognize fraudulent behavior which is what Artificial Intelligence can do. It can analyze more
data without making a trade-off in latency. Most organisations use rules and regulations in coded
form to detect fraudulent behavior. For example, there may be a rule stating that if a customer
adds five cards in 24 hours, flag the card account. However, cyber criminals can easily circumvent
these methods by making lot of accounts to learn rules and work around them.

Machine learning with Artificial Intelligence systems uses to autonomously learn, predict and
act without preset rules. It learns from Data instead of encoded rules. It looks at features of
accounts & transactions instead of few features established in the rules. Machine learning (part
of Artificial Intelligence) make real-time decisions in fast paced financial environment, process
data faster, continuously learn from new transactions, make accurate decisions which reduces
false positives (when a customer's transaction is identified as fraudulent by mistake).

Challenges remain for unsupervised machine learning but as the Finance sector expands and
market continues to demand seamless and speedy transactions, Artificial Intelligence will
play a expanding role in preventing fraud. 


   


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