Think Financial Data Security Requires Off-site Tech? See 3 On-Premise AI Use Cases in Banking
By Kyle Hoback
Banks and other financial institutions around the world may be reconsidering their data security after news that a major cloud-focused bank was hacked via its Amazon Web Services (AWS) infrastructure. As a cloud infrastructure is often thought essential for the future of artificial intelligence in banking, large institutions weighing robotics and AI technologies may be feel deadlocked trying to innovate while maintaining control of data.
How can change agents leverage the promise and capabilities of AI in the banking sectorwithout shifting sensitive data off-site? WorkFusion’s Intelligent AutomationCloud provides enterprises the ability to transform their operations with machine-learning powered Intelligent Automation — all without the need to send data outside their firewall. On-premise AI is a proven reality in a vast range of processes, including Know-Your-Customer (KYC) Identification Validation, Standard Settlement Instruction (SSI) updates, and management of Customer Inquiries. Let’s look at these in more detail.
KYC Identification Validation
To adhere to KYC regulations, when any customer seeks to open an account, almost all financial institutions must obtain and verify identity documentation, such as driver’s license and passport. This data is inherently sensitive and can jeopardize a customer relationship if released. Where financial institutions today must manage hundreds of ID types — across countries, states, and other jurisdictions — AI cuts across the various layouts of these documents to gather key information. Other Intelligent Automationcapabilities navigate institutions’ KYC and other systems of record, reconcile KYC sources of data, and route rare exceptions to operators to ensure high accuracy. Even in financial institutions with hundreds or thousands of applications each day, it’s common for WorkFusion customers to see a 70% increase in employee productivity after implementation, even in a fully on-premise environment.
Standard Settlement Instruction (SSI) Updates
SSIs are shared between financial institutions to expedite domestic and international payments with details about currencies, accounts, routing institutions, and various details. While accuracy of SSIs is vital to the efficient exchange of money, the method of sharing SSIs is often uniquely formatted PDFs, bank-to-bank emails, or unstructured SWIFT Query messages, limiting the ability of rules-based automationapproaches such as Robotic Process Automation(RPA). Intelligent Automation, however, can handle the judgment required in the work. Financial institutions that employ large teams of people to handle SSI Update volumes often see an automated solution with WorkFusion cut time spent on each document from 30 minutes down to only 30 seconds, without sacrificing quality — and again without the need to send data outside the firewall.
While financial institutions, like all enterprises, are increasingly seeking to provide more digital methods of communicating with their customers. However, in many scenarios, end customers simply send various requests via email. With hundreds of types of requests, and numerous corresponding actions that must be performed for each type, the abilities of AI to understand the various nuances of individual emails — the intent of the request and each request’s relevant data — is critical for automation. Applying AI and Machine learning in bankingwith Intelligent Automation software for customer inquiries typically automates over 60% of what was previously done manually, cutting customer wait times from weeks or days down to hours or minutes, and with WorkFusion, no need to send data externally from the organization.
These three examples of artificial intelligence in bankingare just a few of the many AI use cases in bankingfor business process automationwhere WorkFusion customers achieve high operational impact inside the security of their firewall. With WorkFusion’s Intelligent AutomationCloud, there is no need to send data off-site to implement machine learningand achieve the benefits of using AI in banking.
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Originally published at WorkFusion.