With banking frauds increasing at an atrocious rate, focus on fraud analytics and advanced machine learning algorithms has escalated. A rise in online transactions due to increased usage of e-commerce platforms, not to forget the numerous payment methods that have entered the payments market at a heightened rate, has increased the probability of data breaches and frauds. The tech-savvy hackers, fraudsters and imposters are well equipped with various hacking software and techniques which enable them to extract sensitive private user information from the bank servers. Such new age frauds have left financial firms scrambling as it hinders the profits of the company.
Banks are adopting proactive measures to mitigate the growing risks and challenges by incorporating data analytics. Big data and fraud analytics have given a new perspective to behavioural analysis and has enabled real time detection of malpractices.
How is data analytics combating bank frauds?
Identifying patterns
Certain banking activities such as customer transactions, cheque deposits etc have patterns associated with it. With advanced data analytics, these patterns can be analysed to decipher potential fraudsters. Unexpected transactions and suspected customer behaviour can be represented in real time by data analytics. For example, a notification is sent to the bank whenever a person transacts with his/her credit card from one particular device. If during the day, several other transactions take place through different devices, a flag is raised at the server, alarming the banks about a fraud.
Analysing data from multiple sources
Structured and un-structured data is integrated to provide a holistic picture to the financial firms. To get a 360-degree overview about the customer, data analytics algorithms combine the two forms of data to provide critical insights about the fraud. A banking fraud is generally pulled of successfully thorough a series of transactions or a group of people connected together. The transactions which seem normal to human beings are effortlessly tracked by data analytics tools, driven by machine learning & AI, to uncover fraudsters.
Quick problem solving
A swift response by the banks and financial firms to address the fraudulent transactions gives a boost to customer satisfaction. By harnessing the power of data analytics, banks are able to detect frauds quicker than ever. Sophisticated data analytics tools aid in reducing the risk of a fraud going undetected. With banks incorporating data analytics at their backend, customers feel empowered to fearlessly indulge in online transactions. Since, the world is accelerating at a high pace due to digital transformation, having a comprehensive and secure banking system is the need of the hour. AI has truly disrupted the financial and banking sector by enhancing customer personalization capabilities.
Conclusion
Advanced hacking techniques enable fraudsters to innovate new techniques of committing a cybercrime, which costs the banking industry millions of dollars. Data analytics can help catch fraudsters on the go. With the capability of processing millions of transactions in real time, data analytics has definitely helped financial firms and banks save tonnes of money, which they could have lost to fraud. Financial institutions are relying on market research and data analytics companies to predict future market trends which has aided them to strengthen their strategies.
