Banks Need to Think Outside the Box
While AI investment has been steady over the past few years, the recent COVID-19 crisis has triggered a substantial uptick in actionable use.
As noted by Fortune, however, emerging use cases may present a challenge for AI tools that rely largely on historical data for context and decision-making confidence. The more current conditions diverge from past processes, the more challenging it may be for AI systems to find correct answers.
Including a human to review and assess AI recommendations can keep tools on track and help develop more effective responses to immediate concerns. This offers a best-fit solution for financial firms: Advanced AI tools working alongside human observers to address existing concerns around client data privacy and potential decision bias.
Here are a few examples of this approach in action.
How AI Can Manage Banking Microservices
The number of brick-and-mortar banking locations has been steadily shrinking for years. Current conditions have simply accelerated the process, pushing even more clients onto websites and mobile apps.
As a result, it’s critical for banks to deploy “microservices” — such as digital check imaging and account authentication — that help streamline the user experience. But the sheer volume of access and deposit requests poses a potential problem: If accounts are compromised or checks improperly scanned, customer satisfaction will suffer. Small-scale AI solutions offer the potential to manage individual microservices at scale and ensure service delivery on demand.
AI Can Help Banks Deliver Personalized Content
There’s also an increasing need for banks to deliver relevant, curated content to clients via mobile applications or web services. This might include personal loan or credit card offers, investment advice or financial management webinars to help customers weather the current crisis.
Here, AI’s historical focus offers a key advantage. By allowing intelligent tools access to client records and customer service histories (with customer permission), banks can craft targeted marketing initiatives that speak directly to consumer needs.
More Banks Use AI to Fight Fraud
As banking services shift online, they bring a potentially problematic companion: Fraud. As noted by Forbes, phishing attacks are on the rise as scammers attempt to infiltrate consumer accounts via fake COVID-19 relief efforts tied to banking trojans, making digital fraud detection critical to consumer confidence in financial firms’ data due diligence.
AI’s penchant for pattern analysis makes it an ideal tool in the fight against fraud. For example, AI tools can detect anomalous user behavior — such as multiple, high-value transactions in a short period of time — and then report these findings to bank staff for immediate intervention.
AI Eliminates Entry Errors for Bank Employees
Humans struggle with consistent, error-free data entry. Artificial, autonomous processes do not. Equipped with specific guidelines and functions — such as optical character recognition and document database access — AI solutions can quickly capture and collect client data from investment forms or credit applications and ensure this information is properly matched to appropriate personnel for follow-up.
As noted by PYMNTS, AI can also leverage this data to create credit score and risk assessments to both streamline the approvals process and ensure advisers have access to relevant data to make effective investment recommendations.
AI Is the Future of Banking
Artificial intelligence offers big potential for banks — from microservice management to curated content, improved data security and reduced entry errors — making it possible to both address immediate pandemic problems and deliver on the promise of permanent digital priority.
While there’s no single route to successful implementation, there is a simple rule for smart AI investment: Function comes first. By identifying specific operational use cases and deploying AI to deliver key outcomes, banks can build digital-first foundations based on critical functions that both improve employee efficacy and enhance the end-user experience.