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Financial services companies today go to extreme lengths to combat fraud and cyber-attacks on their digital infrastructure. In 2018, the global payment card fraud losses amounted to a staggering USD27.85 Billion worldwide.
From small cyber threats focusing on credit card shopping fraud to international drug and crime rackets, there has been widespread damage caused to financial services firms on account of fraud.
In 2012, HSBC was handed a record USD 1.9 billion fine for failing to prevent money laundering in its network largely exploited by worldwide crime and illegal business entities with links even to terrorist organizations. In 2019, one of North America’s largest banks, Capital One, was handed a USD 80 Million fine for a security breach that led to the leakage of sensitive data of nearly 100 million credit card applicants.
Consumers are increasingly being made aware of the risks posed by fraudsters. At the same time, a need to improve security mechanisms by financial institutions in their digital channels to combat fraud more effectively arises. Firewalls and automated rule-based threat identification systems have been effective in the past. However, as consumers strive for more diverse digital channels for their monetary needs, financial organizations need to bring in more autonomous security mechanisms to cover the entire spectrum of their services.
A key technology that can help financial service companies to combat fraud today is machine learning. In simple terms, machine learning is an AI capability that helps computers learn and respond to a situation in ways similar to how humans would and, in due course of time, improve their responses autonomously from the learning exercise.
Nearly 72.2 % of financial institutions with over USD 100 Billion in assets today use AI to help detect payment fraud. Hence, machine learning is no longer an experimental concept that works in theory, but one that is already demonstrating practical application in the financial services industry.
So, how can machine learning enhance security and combat financial fraud? Here are four ways:
Deeper anomaly detection
Machine learning is an ideal mechanism to analyze and infer meaning from large unstructured data sets.
Financial service firms are known to handle large unstructured data sets almost daily. We have seen how they utilize analytics to derive insights from unstructured data. With machine learning, it is possible to identify anomalies present deep inside these unstructured data sets. These anomalies are often fraudulent transactions or manipulated transactions, which may be difficult to spot with manual inspection, but machine learning helps in tracking signals of unusual patterns in data.
JP Morgan makes use of an advanced machine learning-based early warning system to help track suspicious traffic patterns like URLs that may have been created deliberately as part of a mass phishing attack campaign.
Using machine learning, financial services companies can monitor their growing business volumes with a smaller expert team of analysts who could now safeguard their digital assets from further attacks and segregate anomalies and treat them separately.
Speed and scale
We have already seen how financial services firms use Robotic Process Automation (RPA) to speed and scale up onboarding time for customers. As more customers begin to use an organization’s financial instruments, security measures also need to catch up in speed and scale. Machine learning brings about this transition wherein; it brings an instant response and defense mechanism for financial firms to fight fraud autonomously before they cause damage rather than having to deploy human agents to find and fix issues after an incident has occurred. Machine learning-based solutions can analyze millions of transactions in a matter of seconds and identify suspicious events and further assess their risk to alert relevant stakeholders about looming threats.
Shared intelligence
Financial fraud is not something that occurs in a particular geography or targets any particular financial institution. It is a global phenomenon wherein sensitive financial data of customers across the world is often targeted by fraudsters using a variety of channels and through a host of methodologies. It is often impossible for a single bank or financial services firm to train their autonomous security systems to keep track of all potential frauds as their defense mechanisms depend on insights gained from historic data available with the firm.
By creating a shared platform for financial fraud intelligence, firms can enable better sharing of information related to newer fraud types, data about imposters, and much more. This shared intelligence can be easily grasped by machine learning algorithms of individual firms and subsequently translated into actionable insights for protecting their digital assets from fraud. Without machine learning, it will be impossible to set up a secure defense mechanism quickly based on such a large pool of shared global data about fraud incidents.
Intelligent Customer Authentication
Financial transactions often occur under the supervision of several authentications and compliance mechanisms. But often, these activities require a lot of manual supervision, which is a time-consuming and error-prone activity, and errors can lead to negative consequences. Incorrect customer data such as misspelled names or wrong instructions inferred from customer queries, submissions, and email communications can create challenging conditions in the future when transaction authentications are performed. Machine learning can be the key differentiator in this regard by helping financial services firms authenticate customer transactions autonomously. Using advanced NLP to decipher textual queries and analyze customer interactions and anomalies, it becomes easier for firms to verify the authenticity of a transaction.
This eliminates the risk of delays or fraudulent wire transfers, which were estimated to be worth USD 211 Million in the US alone for 2019.
Machine learning can be a solid defense mechanism for financial services firms in combating the rising incidents of fraud. The more data – structured and unstructured – that firms can procure for training their ML algorithms, the greater will be the defense they can build to protect their digital assets. With more countries increasing penalties for fraudulent activities carried out with or without the knowledge of financial services firms, and increasing regulations on sensitive customer data, ensuring that their digital channels are autonomously guaranteed for secure and fraud-free business is a vital goal for financial firms. With machine learning, this goal is one step closer to the net.
