psKINETIC

Automation & Digital Transformation In Asset Management

“Asset managers are harnessing the tools, expertise, and infrastructure needed to turn data into actionable insights that can drive growth in investments and their investor base.” [Source BNY AM]

Global asset managers need to compete in an eco-system that is increasingly data-driven. Changing demographics and client expectations require leadership teams to re-think their target operating models to meet these new challenges. A holistic change and digital transformation program should become a priority to transition the asset management enterprise into a lean and agile state at an operational level. To facilitate the change program, it is crucial to think about operational agility and the technologies required to achieve the business objectives, efficiently, and with the least disruption.

Change and transformation programs can be challenging and typically, may face some internal resistance. It is vital that senior stakeholders in the front, middle and back-office buy into the program. Each functional area will have different challenges and priorities, so the overall alignment to the strategic objectives of the business is an essential element for a successful outcome.

Data

Data must be the lynchpin of the exercise. Data is the cornerstone and foundation of everything we do, whether in the front, middle or back-office. One of the biggest challenges faced by asset managers (and across the entire financial services sector) is the use of legacy technology and the disparate data sources used across the different functions of the business.

Data Example: Global demand by investors for ESG continues to increase, with AUM totalling US$40 trillion, up from $US12 trillion in 2012. [Source BAML]. However, one of the main challenges is associated with inconsistencies in the data used to research investments. Different data providers use different methodologies, have different calibration methods and may be subject to ad-hoc manual adjustments. In practice, this means that companies will have different ratings depending on which data is used.

Therefore, analysts and investors typically choose several data providers, extract this data, and then carry out their research based on internal best practices or different investment mandates. The ESG data should be centralised and harmonised from a central point. As it stands, data aggregation and harmonisation processes can typically be considered inefficient, primarily because of data inconsistencies, manual inputs, and sometimes poor data governance. This is just one area where intelligent automation would pay immediate dividends.

Data is an asset, and it is essential to set out the master data management strategy to ensure that the enterprise has access to a 360° view of the information required to run and grow the business. It is essential to know where it sits at a functional level, who can access it, and who can make changes. There are significant and tangible benefits of getting your data strategy right, as this is your starting point.

  • Harnessing data and storage into a data warehouse or lake will allow the enterprise to scale its operations across every business area.
  • Data represents value, and this data means de facto better-informed decisions across the investment process.
  • Management information and insights required to run the business more efficiently can be accessed in real-time.
  • A properly thought-out data strategy will allow asset managers to identify and reduce operational inefficiencies and risk.
  • Data should be used to improve client experience and lead to higher levels of employee satisfaction.

Agility also means speed, and consideration must be given to how you extract data from multiple sources while reducing errors that are often associated with the manual inputting of data. Further, it is operationally inefficient and costly to hire smart people to carry out tedious manual work that can be accomplished through automation.

Automation

A strategy for automation should already be an integral part of your digital transformation planning. We must not lose sight of the fact that change management must take process optimisation (from a business and functional perspective) into account when re-thinking the target operating model. To illustrate some of the benefits of automation, I have included some customer KPI’s that clearly show some of the benefits that clients have realised.

  • 95% Process compliance.
  • 100% Real-time reporting to leadership.
  • 75% Reduction in processing time.
  • 80% Reduction in manual tasks.
  • 65% Increase in productivity.
  • 60% Faster processing time.

As well as the obvious advantages of native automation technologies such as RPA, it is also essential to be aware of some of the common mistakes that occur across the sector.

BOT’s have typically been deployed in areas of the business to fix tactical automation needs. In essence, replacing humans who would otherwise be carrying out repetitive manual tasks. The advantages of automation to the enterprise are:

  1. Cost reduction by increasing operational efficiency.
  2. Increased levels of employee satisfaction.
  3. Processing speed, error reduction and consistency.

It is also important to point out that BOT’s do not constitute a significant capital investment.

Be Aware of the Limitations

  1. RPA is limited in future scope and can only take you part of the way on your digital transformation journey.
  2. To extract real value, you will need to think about end-to-end automation and what that looks like.

RPA should not be thought of as a panacea, given that you will probably be tempted to automate areas of the business considered to be the low-hanging fruit. RPA can be perfectly successful, but, in the end, it is likely that as a stand-alone technology, it will most likely fail to align correctly to your digital transformation roadmap. As you think about RPA, it is a great technology but does what it says on the tin, no less and certainly no more. As levels of complexity increase and you try to scale the solution, you will most certainly hit a roadblock.

If RPA is currently being used to automate some of the business processes, you may already be thinking about what next? To properly align the automation projects to the change programs strategic initiatives, you will need to consider the following:

  1. Start by being deliberate about which parts of the business process you want to automate so that the end-to-end process works cohesively.
  2. Building a centre of excellence which will help you to avoid one of the common pitfalls which is the creation of automation silos.

Then think about:

  1. Data orchestration.
  2. Business process improvement.
  3. Workflow
  4. Handling exceptions & rules.
  5. Integration and use cases for AI.

Once you start factoring in these elements, you are having to think about data orchestration and intelligent automation as they work together to build the end-to-end automation you need to accomplish your digital transformation goals.

From Automation to Intelligent Automation

For wealth managers, asset managers and asset servicers, intelligent automation provides an opportunity to address current challenges and create a far more efficient, effective, scalable and controllable operating model [source: EY].

The article has evaluated some of the advantages and limitations associated with using RPA as a standalone tool. It is equally important to consider how to apply AI as well as the impact and use cases available for process optimisation and improvement.

There are many well-documented use cases for AI in asset management, these include portfolio management, portfolio optimisation and alpha generation, risk management, trading, transaction cost analysis and investment governance.

The benefits for client services and product distribution are also well known, after all, one of the reasons for starting the digital transformation process in the first place is client experience.

Example of AI use case for compliance monitoring. Understanding communications that flow in and out of an asset management firm on a daily basis may seem a little daunting. As MiFID II came into effect, portfolio managers had to re-evaluate how they received investment research from sell-side providers. When an investment manager receives unsolicited research from an organisation, this can be viewed as an inducement by the regulator. An AI-driven solution can classify research, automatically flag a potential issue to compliance who can then investigate and act to protect the firm. AI can be applied to many areas of compliance including MAR or market abuse regulations AML, KYC etc.

Setting Success Criteria

It is essential to properly define the success criteria for each automation project before you begin. As a starting point it must be about how you harness data and turn the data into actionable insight, to drive growth in investments and the investor base.

We have seen that it is also about process optimisation across the entire value chain and how it should be aligned to the strategic imperatives of the business. Success criteria can also be measured in terms of ROI and operational efficiency as you move towards operational excellence because of the end-to-end automation and added intelligence. Some success criteria will inevitably be an intangible benefit, an example of which is employee satisfaction. Hopefully, this will translate into talent retention over time, so it is important to measure the level of user adoption. One final success criterion that can be benchmarked effectively, is the reduction of operational risk across the enterprise.

Final Factors to Consider.

Your automation projects should not evolve into a black box and the domain of a few experts, this represents a risk to the business. Your resource model needs to consider both project and support once you have some solutions in production and need to make changes. As part of the overall strategic planning, make sure that you select the right strategic partner who have industry experience and deliver the result based on the agreed success criterion.

Within the world digital transformation and automation projects it’s all too easy to get stuck in analysis paralysis. As psKINETIC we believe in the 90-day rule to take a project from conception to production. The only way to learn is to get your hands dirty, start making progress and gaining the organisational learning from implementing automation.

For further details contact

David Landi – Capital Markets Lead
E: David.landi@pskinetic.com
www.linkedin.com/in/david-landi/

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Automation & Digital Transformation In Asset Management

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