Estimated reading time: 2 minutes
AI and Machine Learning have been pivotal tools within digital systems of this era. Virtually both of these tactics have done much to impact various industries and vertical markets. There is much that comes along with the intelligent decision making process that creates new and innovative services that are continually improving customer experiences. The banking world has grown profoundly more digital over the decades, what with mobile devices being brought into the game, there is much threat to fight. That being said, there is a much larger need to remain on the defensive side in this scenario.
Analyzing the Infrastructure
AI and ML can work to ensure that security infrastructure doesn’t actually compromise the customer experience. There is really no need to see blocked transactions due to suspicious activity, because that action really does infringe upon the user. Yes, this can be a quick fix, but if anything it is definitely quite an inconvenience. Often times these restrictions can push customers to jump ship to other banks for their services. When working together this duo possesses the capability to pull vast amounts of data at one time, in real-time. Such data can analyze the device in use one’s transactional history, the customer’s location, and other very important data that helps to paint a detailed picture of the user and their transactions, including any risks involved.
AI-powered tools are utilized to help humans spot security threats in a quicker manner (including lag in app operations and even suspicious activity in accounts), especially those that require additional authentication credentials. Such higher risk activity within the banking industry might look like an unusually large transaction in a brand new location that seems fishy. Think you’re on vacation and forget to inform your bank that you went away on a trip, so you might be required to undergo some multi-factor authentication such as a facial recognition scan or a thumbprint read, to ensure you are who you really are.
Managing Expectations on Identity
It’s been found that banks, large and small, widely rely on identification management and the processes on how to get there. With digital banking flourishing, breaches will also surge as well, and to combat bots from leaking personal information across the web, there is much to do when it comes to identity verification. 
By combining legacy identity verification tactics with Artificial Intelligence and Machine Learning practices, banks can achieve extensive, context-aware identity verification methods to prevent identity fraud scenarios and allow customers with more freedoms. What does this look like you might ask? Identity checks can look like ID Document Capture, Cross-referencing biological/biometric collected data (facial capture, ID image, thumbprint, device location, or even an account balance).
Cybercriminals are always looking for ways to hack and infiltrate accounts through loopholes and specific vulnerabilities, so it’s really no shock that attacks are increasing in both online and mobile accounts. In order to best fight these scares, banks need to remain vigilant of such threats, but ultimately can do so with the help of AI and ML to provide better customer service experiences for those who hold active accounts.
