For 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 previous article Asset Management Series – Data First, Data Second 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 – Head of Asset Management
E: David.landi@pskinetic.com
www.linkedin.com/in/david-landi/