With banks and regulators fighting against money laundering—urged by increased public and government scrutinyintelligent automation (IA) is explored as a promising answer to detect suspicious activity. Yet, fighting financial crime is and remains costly. Each year, European banks spend a whopping $20 billion whereas US banks are estimated at $23.5 billion. 

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Despite these major expenses over the past decades, 90 percent of European banks received penalties for AML-related offenses. From a global perspective, financial institutions have been fined about $26 billion over the past 10 years. But next to cost-efficiency—we've seen a 35 percent operational cost reduction in our own automation journey—intelligent automation also offers you great value in analysis and decision-making processes throughout your risk and compliance activities. 

With our five 5 steps to maximize the value of intelligent automation, you'll be able to change your business for the good. Simple, smart, and secure. Read how. 

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is spent by European banks every year to fight financial crime. [Source: Weforum.org]


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Setting up intelligent automation in your business

Intelligent automation can, will, and should play a crucial role in supporting your compliance with anti-money laundering (AML) regulations. Throughout the industry, we've witnessed a hiring frenzy of people working on know-your-customer (KYC) processes, so that banks stay compliant. However, intelligent automation can be used here to take up less complex, repetitive tasks, thereby freeing up time for your employees to focus on deeper analyses.

While many of you are already well-aware of what intelligent automation for KYC processes means, you can further expand the value you create through intelligent automation by considering below principles about the human-machine relationship.

We've identified the following five steps to help you make your automation journey smoother and maximize the value of intelligent automation for your business.

  1. Change key Performance Indicators (KPIs) should change from being FTE-savings focused to Straight-Through-Processing (STP) focused.
  2. Give the Lean methodology a significant role in process discovery.
  3. Involve solution architects early on in process discovery.
  4. Give the people aspect of IA more priority.
  5. Start simple when introducing new technologies and extend deliberately.

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What is intelligent automation?

Intelligent Automation is an automation solution enhanced with cognitive capabilities that enable programs to learn, interpret, and respond. See more.

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Step 1. Change KPIs from FTE-savings focused to STP-focused

Looking at the intelligent automation spectrum, traditional automation technologies like desktop automation and robotic process automation (RPA) have always been seen as cost-cutting tools. This perception has largely to do with banks’ KPIs for automation, which are traditionally set around all forms of FTE savings, such as FTE reduction or new FTE hire restrictions. From this perspective, RPA is viewed as a replacement of the workforce rather than an enrichment.

However, the IA toolkit is advancing, and RPA has become a commodity rather than an innovation. Subsequently, the priorities of intelligent automation Centers of Excellences (CoEs) are changing too.

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To prevent poor feedback on the technical scope of a project, we strongly advise you to involve developers or solution architects in new process activities, from start to finish


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A good example is the application of intelligent automation in KYC. Over the last few years, compliance agendas have attracted more attention as banks face more challenges in complying with AML regulations, mainly due to workflow inefficiencies and siloed structures within your compliance capability. In other words, the key challenge here is to meet AML compliance requirements while managing costs and delivering an efficient client experience.

When transforming your KYC function to comply with AML regulations—against low costs, and including effective client experiences—we see that the application of IA in the end-to-end KYC workflow has proven to be the right answer. Be it Optical Character Recognition (OCR) in client onboarding, RPA within name screening and adverse media screening, or machine learning (ML) within due diligence and risk rating, IA technologies allow you to transform time-consuming and inefficient KYC processes to STP in the end-to-end workflow.

Such an application of IA within KYC allows you to standardize processes against high-quality standards (one way of working and first time right), reduce the average handling time of the end-to-end KYC process, and use the workforce for more value-adding activities such as escalations, onboarding decisions, and client-facing activities.

Step 2. Give the Lean methodology a key role in process discovery

Lean Six Sigma and RPA fit like a glove. Its core is about reducing the manual workload by eliminating waste throughout the process. Also, Lean Six Sigma is defined by practices like simplification and standardization process steps, which are also a key component of RPA. 

Lean Six Sigma process analysis focuses on the end-to-end process, whereas a traditional RPA journey rather includes automating a particular process step without the process redesign. We, therefore, recommend you to apply Lean concepts before implementing RPA to fully reap the benefits of both techniques. Process excellence—optimization or redesign—remains crucial in this period of robotization.

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We've seen a 35% percent operational cost reduction in our own IT automation journey.

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Step 3. Involve solution architects early in process discovery

Within new process discovery, business analysts (BAs) in CoEs are usually responsible for following up on automation requests submitted by the business (managers). When moving from the discovery to the delivery phase, we often see that from the start impediments arise because of poor feedback on the technical scope of the initiative.

To prevent that from happening, we strongly advise you to involve developers or solution architects in new process activities, from start to finish. Whereas BAs and managers usually see the business benefits from an automation initiative, they can have a blind spot on technical hurdles. Developers and solution architects solve this gap by challenging the solution design by considering the following aspects of a process:

  1. The technical need for the robotics solution: Is there already a structural solution at hand with Application Programming Interfaces (APIs) or automation within existing applications?
  2. How the solution robot interacts with applications: Are there any APIs available instead of getting the information through the front end?
  3. Security access framework: Is there multi-factor authentication or a Citrix layer in place?
  4. IT Infrastructure: Are applications accessible and available in the cloud?

The four aspects mentioned above could raise impediments or act as potential road blockers in case you don't clarify or solve them before kickstarting your IA project. Our experience in several intelligent automation CoEs has taught us that the involvement of developers or solution architects directly from the start of your project will significantly reduce your delivery cycles and, therefore, result in quicker value realization.

Step 4. Give priority to the people aspect of intelligent automation

Process automation is generally associated with replacing jobs. As described in the first step, we see IA as an enrichment rather than a threat to the workforce. We generally see two areas where people and technology strengthen each other the best:

i. New process discovery

Where RPA CoEs are business-driven, management usually decides where automation initiatives are being deployed within the organization and where the impact is felt. As this top-down approach is a great way to create value while complying with corporate strategy, we see that management is not always choosing the options that fit the best.

Understandably, we observe a new trend in the market: an increase in bottom-up process discovery. In other words, workforces and operations fill the backlog with candidate processes for robotization and play a central role in driving the direction of the automation initiatives. Such a bottom-up approach does not only allow you to identify processes that are operational pain points but also strongly stimulates workforce adoption of human-robot collaboration as your employees have gained first-hand automation experience and have become automation champions themselves.

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ii. 1+1=3

As humans, we possess cognitive capabilities that are unmet by machines. Think about capabilities like human judgment and empathy. Machines are, conversely, better able to quickly execute repetitive tasks and quick decision-making based on large data sets. To fully exploit the capabilities of both humans and machines, they should not be presented as a threat to one another. 

On the contrary, humans and machines have unique possibilities to collaborate, form relationships, and with that, complement each other to increase the scope of possibilities to solve complex problems to the benefit of your bank and your customers.  

As shown in the image below, humans can enable machines by training for high performance, transparency, and sustainability. Moreover, machines offer humans personal assistance, to give humans unique channels to capitalize on their skills—think about virtual conferences or long-distance surgeries and data-driven insights. In the near future, when humans and machines truly join forces, new opportunities emerge, and capabilities that were deemed impossible will become the new standards in both banking and beyond.

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Step 5. Start simple when introducing new technologies and extend deliberately

The intelligent automation toolkit has grown considerably over the years. The origin of intelligent automation lies at robotic process automation, a technology that is often, as previously said, described as a commodity nowadays. At the other end of the spectrum lie less mature technologies like machine learning. 

The urgent need for efficiency in the KYC domain means that it's crucial now to fully realize the potential of these new technologies and their capabilities. However, it can be quite a challenge to maximize their value as many technologies find themselves in different maturity stages.  Needless to say, we see large discrepancies between the theorized value of new technology and the value it adds once implemented. 

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To ensure that innovative technology will deliver the expected value, you'll need to start with building a strong foundation on which you build the remaining infrastructure. Trying to do too much at once often leads to higher costs and lower benefits. Therefore, we advise you to start small when implementing new technologies.

First, ensure that a base process is present, built using existing mature technologies. Then, when the base has proved to be stable, you can start extending the scope by introducing new technology to the process. The stable foundation ensures that you can focus all attention on successfully implementing the new technology.

To realize maximum value for the KYC domain, your focus should be on directly applicable automation technologies like RPA and Natural Language Processing (NLP). When the less complex processes have been automated and intelligent automation has a significant footprint within the domain, the automation toolkit should be consciously scaled up. This ensures a quick realization of added value and prepares both the business and IA CoE for the implementation of less mature technologies in the KYC domain.

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Humans and machines have unique possibilities to collaborate, form relationships, and, with that, increase the scope of possibilities to solve complex problems to the benefit of your bank and your customers.

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Ready, set, scale

Intelligent automation can, and should, play a crucial role in ensuring compliance with Anti Money Laundering (AML) regulations. An increasingly large human workforce is fighting against financial crime by detecting suspicious customer or account activity, analyzing vast amounts of transaction data for evidence.

When it comes to rooting out financial crime, intelligent automation is a critical requirement for developing an effective E2E process as well as a 360-degree risk view. The abovementioned 5 steps help senior leaders and decisionmakers in the anti-financial crime space to identify and define the strategic initiatives moving forward. If you have any questions about what we've told you above, feel free to reach out to us. We'll be pleased to help you out and support you in your automation quest. 

Paul Weiss

Management Consultant – Payments, The Netherlands

Axel Haenen

Technology Consultant – Accenture Strategy & Consulting, Financial Services

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