As banking and banking related services are an essential part of the government, it can be considered as one single organisation facing the general public who are at various levels or backgrounds which can be ranging from most disciplined ones to the most susceptible ones in terms of the risks being exposed to. Its duty of this organisation to keep the checks and balances, the forensics intact to minimize the losses as a department of its own.
Over history, the reach of banking and its operations to the local public has become more efficient, more exhaustive, simpler, scaled easier and automated. These are the fruits of the phenomenon of digitisation, information technology services, where the technology at large has taken control of banking operations. However, when we have such reliance in technology becoming a foundation for a bank to operate, there is always the threat of risks which the same technology possesses in scuttling its operations. This is one area of technology risks among the whole spectrum of risks which a bank is exposed off, which this section addresses.
As per the latest data, the global financial crime is massive in scale and accelerating in pace with an estimated value of USD 4.2 tn. Another study estimated that a typical organisation would lose 5% of revenue to fraud each year. The same number would amount to trillions when it comes to the size of this fraud which all banks in the world are vulnerable to. With the thief becoming more smarter a variety of fraud happens at various levels in banking which could be classified broadly into:
While we see the characteristics of the financial fraud in its unfold, one aspect is evident, banks or banking over time is becoming a warehouse of data of unimaginable magnitude in terms of customer behaviour, spending patterns and also the way a fraud is conducted. If the data can be analysed and patterned to the specific ways of how the fraudster conducts the fraud, the scope of AI comes into picture. With the current outing, a handful or products are available in the market that sequentially targets this space and builds products using this model. The fruits are impressing in terms of how the statistics show. The technologies are Machine Learning, Neural Networks, Deep Learning, Data science.
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