Process Mining | Intelligent RPA | Cognitive AI
Digital banking

Banking Use Cases

In banking, there is automation potential across the entire customer management lifecycle.

Know Your Customer (KYC), Customer Due Diligence (CDD) and Anti Money Laundering (AML) are some of the common use cases

Keeping up and complying with regulations like Customer Due Diligence, Know Your Customer and Anti-Money Laundering is expensive and challenging. RPA will automatically acquire, enhance and deliver the precise data you need to comply from any internal or external source—and it can check far more data points in a shorter period of time to provide you with a more comprehensive assessment.

Know Your Customer Procedures

Initial verifications

    • Document validity verification
    • Ultimate beneficial owner verification
    • Source of wealth
    • Sanctions and political exposure verification 

Periodic verifications

    • Customer portfolio screening
    • On change event screening
    • Due diligence
    • Sanctions and political exposure verification

Onboarding

    • Customer document reading
    • Preparation of contractual papers

Closure

    • Dormancy
    • Closure initiated by the bank from due diligence
    • Zero balance and dormant account closure
    • Closure at customer request

Client Set Up

    • Data set up in product specific systems
    • Special pricing in case of salary conventions

Customer Maintenance

    • Static data change (ID change, business activity)
    • Registration address change
    • Contact info change
    • Ultimate beneficial owner change
    • Block and unblock due to legal events

Let’s now see some industry-specific examples of automation

  1. Verification of Loan Application Documents

To verify loan application documents, the bank’s employees had to manually check different web portal documents and related information for home loan applications, then collate everything into a single file.

They were spending too much time processing more than 100 loan applications per week and needed to expedite the resolution process for customers

The UiPath Robot was used to quickly open different web portals and verify information before sending an email to the person who requested the documentation for a decision.

20 hours saved per week
Shortened time to client response.

2. Rejected Direct Debit Management

Direct debit is a service offered by banks to ease the collection process of service or utility providers. In short, beneficiaries agree to pay the amounts that the providers request directly to the bank. However, mostly due to insufficient funds, there are many direct debits rejected by the system that need to be processed manually.

The company’s rejected direct debits management process wasted employees’ time by requiring them to manually check 800 to 1.000 transactions during the first four hours of each day. Based on a printed paper transaction report, they analyzed each customer with an overdrawn account and decided if the bank would honor or reject payment. Unfortunately, process rules weren’t clear and bank fees were charged inconsistently.

The UiPath Robot was used to capture the report and convert it to a spreadsheet, grab customer account information from the core banking system, analyze it, and – using a core set of rules – decide to honor or reject the direct debits. This increased accuracy and allowed the paper-recorded client histories to be added to the customer relationship management (CRM) system.

 

  • Implemented within 7-9 weeks
  • 95% automation rate
  • More than 25.000$ of monthly revenue gain
  • Turnaround time down from 16 hours to 6 hours daily

 

 

3. Fraud Detection

Fraud detection is a process done for each loan approval, by a different person than the client representative. It consists of bringing together and interpreting data from different sources, inspecting documents and monitoring any suspicious signs.

The bank had an insufficient amount of resources allocated to their fraud detection process, which caused undue stress and work for their team, generating inefficiencies, frustration, and errors.

The UiPath Robot accessed up to 15 applications and databases, both internal and external, for potential signs of suspicious activity among the bank’s clients. It then compiled this information in a report for review by a human fraud analyst. This decreased the required work of employees and streamlined fraud protection.

 

  • Processing time for each application dropped from 45 minutes to 20 minutes
  • 1 hour of work was automated to 5 minutes
  • 95% automation of the process steps
  • 0% exception rate for the automated processes

 

 

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