Robotic Process Automation in Financial Services Report 2021

80 Pages

The face of the friendly neighbourhood banker is slowly but surely changing. Robo-advisors, for example, are eating away 50-60% of new jobs in the wealth management space. Welcome to new age banking. The opportunity to drive higher efficiency, better risk control and lower costs via robotics and artificial intelligence is here to stay. Automated threat intelligence, fraud analysis and quality assurance, predictive analytics, voice recognition and automated response are exciting new areas of focus. Add the convenience and intelligence of bots to customer experience and we may well see a new era of banking. No surprise, then, that around $9.7 billion was spent on cognitive and artificial intelligence in the US during 2017 alone.From a bank’s strategic perspective, a review of the way forward involves: Which areas drive automation through robotics and why? What is the impact that is to be driven and how? How should the right RPA partner be chosen?

The primary focus areas that typically qualify for driving robotics are mostly those with highly mechanised operations, with limited decision-making requirements. These are typical back-office functions, validation and verification activities and monotonous operations.ICICI Bank, for example, has reportedly over 200 software robots to emulate, automate & perform repetitive, high volume tasks across multiple business process functions including retail banking operations, trade, forex, treasury and HR, with a processing capacity of over a million transactions daily. This is estimated to reduce customer response by 60% and increase accuracy to 100%. Once low hanging fruits are addressed, the approach typically moves to areas that have a better user interface, even while it is focused on highly repetitive activities that tend to clog bandwidth. New generation bots help learn and get better at what they do, driven by intelligent process automation. Good examples here are chatbots linked to social media and websites, providing quick and appropriate responses to customers. RBS recently launched AI chatbot Luvo on a pilot basis, focused on filtering responses and providing appropriate responses to customers. The technology is estimated to reduce investment advisory staff by 200, improve operational efficiency and reduce response time to queries. The more interesting areas of predictive analytics-driven AI, while having a high impact value, tend to require a much higher degree of focus and preparation. Enhancing customer experiences is also supported by innovations across online channels, where conventional experience is protected. US-based Nuance Communications’ tech, for example, is designed to answer questions from Swedbank’s online customers, while simulating a human conversational style, and is also planned for roll-out across its subsidiaries in Estonia, Latvia and Lithuania.

This is where it gets a little trickier. No matter how innovative the approach, or how interesting the proposition is, there must be a measurable impact. There is essentially a dual-axis that the impact gets measured by: Robotics driven initiatives may either drive costs down (efficiency) or improve service quality, convenience, and the accuracy levels (effectiveness) or both.The benefits of RPA could essentially be structured in five broad metrics: Cost reduction, accuracy improvement, productivity and scalability enhancement, quality assurance and risk mitigation. Here are a few examples of AI and robotics applications, and the kind of meas
• Improvement inefficiency: Typical examples here are the automation of operational, mundane activities. In essence, this is to do with reporting, reconciliation, data remediation and such activities. The direct impact here is to eliminate manual intervention, with an automated approach. Cost drivers are direct measures, e.g. back-office mortgage processing.
• Increase ineffectiveness: Accuracy of risk and compliance monitoring, predictive analytics-driven collections, are examples where the impact is more on ‘opportunity savings’ and enhanced revenue via the quality of the process, driven by RPA.
• Driving both efficiency & effectiveness: These are areas that banks target improved customer experience, through higher quality interactions while increasing the degree of automation. Examples are related to account origination, lending, investment advisory and customer service. Increases in throughput and improvements in returns are standard measures here.

The fundamental shift in the approach to driving RPAbased efficiency is not in re-engineering the processes, but automation of the mechanical processes, without changing or replacing the existing application infrastructure. The bank must have a point of view on the objective to be addressed, the volume and scale of automation planned, and most importantly the value and impact that comes of the initiative. The key here is establishing a Centre of Excellence to support a certain line of business, succeeding in a proof of concept, and then pushing this forward on a bank-wide basis with standardised security and governance principles. Automation Anywhere, Kofax, Blueprism, Workfusion, Softomotive, UiPath, Redwood, Arago, Celaton, Ignio, Contextor, Open Connect, Pega Systems, Kryon, NICE, Verint – these are some of the leading players in the RPA space. Pega’s recent acquisition of OpenSpan and ISG’s takeover of Alsbridge is reflective of increased traction in the market. That Blueprism could generate investor confidence of boosting its valuation 4x within 10 months of listing, is also an indication of the direction in which the industry is moving. Solution providers in the universal and core banking space such as Edgeverve (Infosys) and Infrasoft are also making waves in the RPA space. The experience of and support from the supplier or implementation partner is a key success factor. Skilled resources with an ability to drive the primary value proposition with innovative design, and technical expertise and their availability in the markets of deployment play an important role in the choice of the supplier and partner. A word of caution as we move into the era of robotics and automation. These are relatively unchartered territories and so new types of risks emerge. The challenges are more related to compliance, cyber security, data privacy, controls and governance. It is natural, then, that banks who select suppliers offering a better governance framework to test and control, and apply advanced analytics to boost pattern recognition and have well established used cases, will be better off than their peers.

  • What’s new in this edition
  • Section 1: RPA in Financial Services| Executive Summary
    • Challenges for Banks when deploying RPA
    • The future state of RPA
  • Section 2: Overview of RPA Technology
    • Components of RPA
    • Types of RPA
  • Section 3: Key Considerations for Banks when deploying RPA
  • Section 4: Framework for planning an RPA deployment
  • Section 5: RPA Market Landscape
  • Section 6: Current trends in RPA
  • Section 7: Enterprise Approach to Automation
  • Section 8: RPA Supplier Profiles
    • Automation Anywhere
    • Blue Prism
    • CustomerXPS
    • Datamatics
    • Edgeverve Assistedge
    • EPIKInDiFi
    • UiPath
    • Thoughtonomy
    • Expleo Solutions
    • Kofax
    • Kryon
    • WorkFusion
    • Tenjin by Yethi
  • Section 7: Robotic Process Automation | Industry Outlook
  • Robotic Process Automation expert interview

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