The Smart Money Is on Intelligent Automation Shrewd financial services companies are betting on advanced automation tools and cognitive technologies to transform their businesses.

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Overview

Forward-thinking financial services companies are using cognitive technologies and process automation to transform core business functions and improve operations. Robotic process automation (RPA) is increasing the efficiency of insurance application processing and stock trading. Meanwhile AI, machine learning and natural language processing are being harnessed to quickly and accurately analyze large volumes of data for fraud detection or credit approval. In the IT department, automation of legacy system maintenance is freeing valuable resources for digital transformation initiatives. Read about the benefits financial services leaders are achieving and find out how to get started with intelligent automation in your business.

3The Smart Money Is on Intelligent Automation

Contents

04 Intelligent automation: Transforming financial services

05 Complex processes and unwieldy IT environments

impeding innovation

05 Intelligent automation: A continuum of maturity

06 RPA scenarios

07 Intelligent automation in IT operations

07 Case study: Application support and maintenance

08 Case study: Virtual assistant for research analysts

08 Case study: Customer contact center operations

transformation

08 Making it happen

Intelligent automation is enabling major transformation in financial services. It combines automation with artificial intelligence (AI) and other cognitive technologies to handle tasks formerly managed by humans. Financial services institutions stand to gain real competitive advantage by harnessing intelligent automation: Reduce operational costs. Improve the customer experience. Gain key business insights. Process higher volumes of transactions more efficiently and accurately.

Intelligent automation offers the opportunity to optimize processes such as loan processing or insurance policy issuance. But strategic functions such as fraud detection, credit decisions, IT operations and infrastructure management are also well within its capabilities. In its report, “Machine Intelligence and Augmented Finance,” Autonomous Research estimates that AI will save the financial services industry $1 trillion by 2030 in combined front-, middle- and back-office costs. Freeing up budget and talent will allow those valuable resources to be used to innovate and change the business.

Reduce operational costs

Gain key business insights

Improve customer experience

Process higher volumes of transactions

Intelligent Automation:Transforming Financial Services

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Intelligent automation is not a single technology; it’s a spectrum of methods that have evolved over time, with each phase bringing increased sophistication and power. The most basic, early instances consisted of automating simple tasks through a scripting tool or executing processes through a workflow management system. Data must be structured to be interpreted and managed by these systems.

Basic Automation

Automation Maturity

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Robotic ProcessAutomation

Intelligent Automation

Point solutions where certain parts of a process are automated using simple software tools and scripting

Data Formats: Structured

Automation of entire process using tools that support rules-based automation

Latest automation tools utilized in conjunction with cognitive technologies like AI, ML and NLP

Data Formats: Unstructured

Complex processes and unwieldy IT environments impeding innovation

Intelligent automation: A continuum of maturity

Tech-savvy customers demand highly personalized and responsive services. Markets demand innovative products, services and channels that can be adapted quickly to accommodate changes in demand or regulations. Meanwhile, many financial institutions are saddled with unwieldy, inflexible legacy systems that are expensive and labor-intensive to update. This requires companies to apply scarce funds to maintenance that could otherwise pay for digital transformation and streamlining operations. It’s a significant disadvantage when competing with non-traditional competitors such as modern fintechs that don’t face these legacy constraints.

Enter intelligent automation, which can automate those expensive manual processes and bring powerful cognitive technologies to bear for more strategic tasks.

5The Smart Money Is on Intelligent Automation

JP Morgan Chase is using intelligent automation to review and extract data from documents in seconds that would have otherwise required hundreds of thousands of hours of work, according to Forbes in “How Artificial Intelligence Is Helping Financial Institutions.” Similar techniques can be used with loan applications, lease agreements and other decisions for which many texts of different types must be scanned and interpreted.

The Forbes article also describes how Citibank is using big data and machine learning to identify threats to customers and prevent criminal activities such as money laundering.

One hedge fund used intelligent automation to analyze content from some 10,000 published research notes. Checking for consistency between the notes and the ratings assigned to stocks would have been prohibitively time consuming if done manually, according to a Deloitte University Press article, “Intelligent Automation: A new era of innovation.”

Some financial institutions are expanding customer relationships and revenue by using AI to recommend products and services relevant to customers’ lifecycles.

Other uses include developing better investment strategies or improving insurance risk models with AI.

Chatbots are a useful illustration of how adding cognitive technology can elevate an implementation from simple RPA to more complex management of advanced tasks. As described above, basic chatbots are simple and rules-based. However, Forbes notes that financial institutions such as Bank of America are enabling their chatbots with AI to not only perform transactions but also provide financial guidance for clients. Bank of America’s Erica chatbot is designed to learn over time to reduce the need to consult experts for advice on non-standard queries. Other banks are using AI to track customer behavior and preferences to better understand and improve their experiences.

The next phase is robotic process automation (RPA), which is currently one of the most prevalent applications of AI in financial institutions. With RPA, entire processes – usually repetitive and labor-intensive tasks – are automated from end to end using tools that support rules-based automation. Software robots may send emails, open applications, and handle transfers of information from one system to another across application interfaces. RPA solutions are deterministic in nature. Basic RPA solutions make decisions based on the rules they have been given and require human intervention when complex decision-making is required.

More recently, intelligent automation techniques have emerged, using more sophisticated cognitive tools and technologies such as AI, machine learning, natural language processing, pattern recognition and cognitive computing to simulate human thought and handle more complex functions. Cognitive technology-based systems can interpret large volumes of unstructured data and start to define rules and processes to improve them. For example, with natural language processing, the computer can scan documents for certain content and make decisions based on that content. They can also perform analysis to find patterns that would be impractical or impossible to identify by traditional means. For example:

How RPA streamlines core processes for financial institutions

RPA scenarios

Reducing steps in loan processing: A bank implements RPA for consumer loan processing. Previously, it took the consumer loan processor about 20 minutes to handle each application. The work, at least 80 percent of it, was tedious and manual — logging into multiple loan processing systems, credit bureaus and government websites; creating and attaching PDFs, copying and pasting information to check addresses and other details. After the bank implements RPA, those repetitive tasks are transformed to a single mouse click, and processing time goes from 20 minutes to 5. The loan processor receives the loan package in the system and launches a software robot. The bot logs into the systems, pulls the information for the credit report, enters it into other core banking systems, logs into the government website, checks the address and validates the appraisal. Then the bot saves the address check and appraisal PDF to the loan processing system. With the time savings, the loan processor is freed to perform more valuable tasks that impact the customer experience. (Source: The Lab’s report, “Insights from the Lab: Robotics in Banking with 4 RPA Use Case Examples.”)

Improving transaction accuracy and processing time at an investment company: BNY Mellon is an enthusiastic adopter of RPA reported to have had 250 bots in production as of 2017, according to the Insights from the Lab report. RPA is being used to increase the efficiency of trade settlement procedures, reducing the time to reconcile a failed trade from 5-10 minutes to a quarter of a second. The bank has also observed an 88 percent improvement in transaction processing times and account closure validations, with an accuracy rate of 100 percent.

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Intelligent automation solutions using cognitive computing and AI are being used to automate aspects of IT systems maintenance, allowing financial services companies to realize productivity gains and cost savings. These solutions provide:

• Continuous monitoring to detect failures and automatically remediate issues that could cause systems failure

• Intelligent service desk for real-time resolution of user requests

• Identification of patterns and anomalies to uncover problems and suggest preventive action instead of reactive recovery

• ROI-based analysis of tickets to identify opportunities such as rapid deployment libraries and process recording to accelerate automation

reduction in TCO*

30%adherence to business SLAs

100% 40% 25%

Case study: Application support and maintenance

A financial services firm needed a coordinated approach to support users and monitor large application portfolios across nine lines of business in multiple geographies. The company lacked an integrated view of the total user experience and was facing a large ticket backlog. In addition, traditional release management practices were slow to respond to business needs, and there were high costs and risks associated with meeting SLAs for critical transactions.

Atos provided 24x7 application monitoring, user support, application support services and support optimization services. A comprehensive operations automation framework was delivered using SyntBots® – Atos’ Intelligent Automation Platform – and AI. The new technologies detect, analyze, isolate and remediate issues, as well as monitor end-to-end for early issue detection, customer journey mapping and real-time business dashboards.

As a result, the institution avoided $144 million in payment delays and prevented $10 million in corporate payment rejections. They also achieved:

* Total cost of ownership

7The Smart Money Is on Intelligent Automation

Intelligent automation in IT operations

Case study: Virtual assistant for research analysts

A financial research agency wanted to make it easier for its users to search documents when performing credit analysis and digital research. When users were stuck on a process, they had to manually navigate to a document repository and search for the relevant document for help. Atos created a virtual assistant chatbot that leverages machine learning and natural language processing to interpret context from queries, understand the intent from subsequent conversations and respond with relevant documents. Results included: 80 percent faster search, enhanced user experience, easy access to documents and auto-navigation to an exact page in the document.

Case study: Customer contact center operations transformation

A leading European insurance company was struggling to provide seamless customer service for policy inquiries, cancellations and payments through a customer contact center that used eight different application systems. It needed improved reporting and decision-making with lower training costs for agents and the ability to skill staff across product lines. Atos used robotic automation to develop a new, more efficient contact center operations process that lets agents and customers perform self-service from a single screen. Requiring no change to existing systems and processes, the plug-and-play solution reduced costs by 25 percent, doubled the speed of customer service and enabled 25 percent of services to be provided through digital self-service.

An estimated two-thirds of financial services firms are expected to make significant investments in intelligent automation over the next three years. If your firm is among them, there are some questions to consider as you develop your plan and ensure that your implementation of this technology will support your business objectives.

The human factor. The mention of AI and robotics invariably brings fears of “Will my job be replaced by technology?” It’s true that doing more with less staff and reducing manual intervention in repetitive processes are primary benefits. However, the aim of intelligent automation is not to establish fully automated systems or replace human workers. The aim is to free workers from rote, repetitive tasks and give business and technology decision-makers more insights

into and control over their jobs. Intelligent automation enables many workers to perform more creative and meaningful work requiring human interaction and decision-making, or more high-level management of their processes. For example, RPA lets front-line employees maintain and manage their own robots rather than relying on IT staff to perform complex programming. These issues can be successfully managed with proper employee communication and retraining for new roles when necessary.

Setting expectations for savings. With analysts projecting $1 trillion of savings from AI for the financial services industry by 2020, what kind of return on investment can you realistically expect from your own implementation?

Companies often achieve quick cost savings from RPA. The technology is relatively fast and easy to implement, with some configuration but little programming required. RPA can exist as a layer on top of existing applications, requiring no essential change or upgrades to core IT infrastructure. Robots can be installed quickly and updated when processes change. Cognitive automation, on the other hand, may require more programming and must be customized more for specific uses, but may have a greater potential to fundamentally change the business.

Making it happen

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Getting the right start. Doing things right in the initial implementation of intelligent automation goes a long way toward ensuring its success. Experts advise businesses to:

• Align with business objectives. Form a cross-functional team with business and technology representatives who can define the desired end state and how it will be reached. If operational costs are your primary driver, start by creating a baseline for costs. Then measure after your first implementation and subsequent attempts to show savings achieved over time.

• Balance quick wins with long-term strategic impact. First, identify simple projects that will yield maximum impact with minimum investment of time and money. Any repetitive, high-volume sets of tasks that require input and output of data into multiple systems are good candidates for intelligent automation. Select a process or customer experience that you want to automate and then launch a stripped-down version that can be tested to see what works and what doesn’t. Over time, examine processes throughout the company and rethink how they will work in the future, moving from standardized to more complex, customized processes. Some companies abandon intelligent automation projects because they try to do too much, too soon rather than focusing initially on low-risk, high-reward cases. Others have trouble moving beyond the simple, short-term gains of RPA. Long-term strategic success of intelligent automation initiatives requires leadership vision, a focus on reengineered processes and use of appropriate business metrics to determine effectiveness.

• Analyze and standardize current processes before RPA implementation. This is one of the most important activities in intelligent automation, although many companies are tempted to skip it. Define all front- and back-office tasks required for daily operations. Use business process maps to identify sub-processes where robots can be used. Define business requirements and rules, with details on data elements, outputs, sequences of activity and details of keystrokes required to perform the work. And engage employees in the design and implementation of intelligent automation.

• Create sustainable capabilities. Define the new skills that workers will need to support intelligent automation. Create reusable assets, such as playbooks. Develop a complete plan for coaching, training and knowledge sharing among business and technical staff.

• Don’t be limited by the current state. Rather than simply streamlining current processes, envision an organizational structure that best supports achievement of your strategic goals. Prepare a change management program and continue to track new trends and technologies as you harness the power of intelligent automation to reinvent your business.

Companies implementing intelligent automation report substantial average gains:

About Atos intelligent automation for financial services Atos is a trusted partner for many of the world’s biggest financial services brands. Our intelligent automation platform, SyntBots®, uses cognitive computing and artificial intelligence to transform financial services IT and business operations as well as product engineering.

• SyntBots® for IT Operations ensures that IT systems and processes are always on to service mobile and global consumers across all channels.

• SyntBots® for Business Operations provides process automation to free business bandwidth for transformation.

• SyntBots® for Product Engineering enables faster development of new ideas and ensures products are right the first time.

reduction inoperational costs

35%faster

time to market

40%higher

productivity

10%less down time

70%

9The Smart Money Is on Intelligent Automation

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