It’s clear that the digital era in financial services is here. And given the enormous pressures for firms to uncover alpha, it is not surprising that innovative uses of artificial intelligence (AI) and machine learning (ML), digitization and automation are commonly associated with cost savings and making businesses leaner. Those outcomes are true and important, but the innovations yield far greater benefits than just improving efficiencies.
Whether you’re executing quantitative trading or onboarding a new client, the financial services industry is a series of decision-making that’s predicated on experience and subject matter expertise. What if you could augment or automate those decisions? The result is a workforce that’s free to focus on new products and client outcomes.
By reframing your digital endgame, you can leverage technology to not just do the work, but to accelerate how people learn. In other words, you can both scale and automate your business, while also creating a multiplier effect on how you disseminate critical subject matter expertise. Traditional learning techniques may not be able to keep pace with a dynamic business environment. However, by leveraging AI / ML, one person’s knowledge can be distributed across the organization which mitigates key person controls and improves operational resiliency. With the right operating model, the technology can be a catalyst to build, capture, retain and deploy talent.
Understanding your workforce
In today’s rapidly changing workforce, the average tenure of employees is between 3-4 years and only two years among millennials. This means that building and retaining institutional knowledge is more critical than ever.
By 2025, millennials will make up an overwhelming 75% of the workforce and one in three plan to look for a new job.1 This generation – who is considered technologically advanced and passionate about company values and culture - place more emphasis on increasing their skills and development than compensation. A purposeful pairing of the right technology with an operating model that empowers this demographic to take part in transformational work, results in higher engagement and connection to the company. Prudential’s Pulse of the American Worker Survey in March 20212 revealed nearly half of workers (46%) say the pandemic caused them to re-evaluate their skill sets.
So, how does a firm create a symbiotic relationship between its technological capabilities and building a workforce of the future? Here are a few pragmatic steps to start with:
1. Set the tone from the top
A purposeful organizational strategy to support and foster innovation is critical. An executive vision for automation and innovation with a common firmwide understanding can be just as important as executing on day-to-day processes.
However, the tone from the top is not just about communications, campaigns and inspiring the masses. It’s about creating an organizational construct with aligned incentives centered around business outcomes.
Resources should be ring fenced to deliver automation to address real use cases, with the appropriate recognition and rewards for the transformative outcomes. As this may be a significant cultural shift, these actions are required to drive employee buy-in and engagement.
2. Get practical before you go big
Task automation is important and there is a lot of it to tackle. Incentivize teams to migrate away from end-user computing tools like macros and excel through utilization of democratized, configurable applications that digitize their work. These technologies automate at the task and workflow level by capturing, structuring, and transforming their data. This automation delivers significant business value through increasing productivity and eliminating manual efforts. The result is a workforce that is infused with a layer of technology, creating, and storing data and analytics that can be used to augment decision-making.
As an industry, many core, transactional systems have mitigated manual tasks and data entry, but the captured data remains largely underutilized. Additionally, processes may still be reliant on subject matter expertise for exception processing and issue resolution. Machine learning and artificial intelligence can be leveraged to streamline these activities and augment decision-making through anomaly detection and predictive analytics.
Let’s talk about this in practice.
Use Case Spotlight: How automated trade capture creates data to predict settlement fails
While industry STP rates are high in transaction processing, there are pockets of exceptions, where instructions or transactions are received through unstructured mediums. BBH addressed this issue through democratized automation tools - mapping trade templates, extracting trade details automatically and feeding them directly onto its trade platform. Capturing the data in this way reduces the risk of manually re-keying information and increases efficiency. Leveraging the subject matter experts to facilitate this automation created capacity and surfaced a further opportunity for greater efficiency.
Consider an ETF servicing team which is laser focused on monitoring unmatched trades that are in high risk, mandatory buy-in or Central Securities Depositary Regulation markets3 that apply financial penalties for fails. A machine learning algorithm that acts as an early warning system by predicting the likelihood of the trade failing in advance of settlement date could be a major asset for an analyst. This would direct them to take corrective action on the trades most at risk, helping to mitigate penalties and fees that would get passed onto customers and counterparts.
3. Maximize continuous learning through an outcome driven approach
As new tools are introduced, it’s critical to recognize that this may be a cultural shift for some and a skills gap for others. Focusing on business outcomes and rewarding progress will lead to adoption of new tools and repeatable patterns that achieve results. More specifically, this requires businesses to:
- Identify “transformers” who will be accountable for finding use cases and building prototypes or proofs of concepts to demonstrate tangible business value and explore the art of the possible
- Deploy task-based automation solutions rapidly, introducing automation tools as part of daily workflows. This means employees not responsible for development initially will be adopting, using, and benefiting from the tools daily without the pressure of major upskilling. This builds trust and confidence in new solutions
- Provide accreditation and certification to the employees automating workflows to acknowledge and recognize their new capabilities
- Apply what is learned through doing. Recognize how teams are being strategic because of implementing automation. Lessons learned should be implemented practically to accelerate further automation
Getting the balance right
The more you digitize, the more you can create new value streams and ensure greater resiliency within your operating model. As such your talent acquisition strategies and approach will also need to evolve to build your workforce of the future. Successful organizations will optimize the balance of existing subject matter expertise with technical capabilities that propel automation forward through a purposeful organizational construct and repeatable recipe for transformation.
We’ll be sharing more insights on attaining and combining the ingredients within your workforce for such a recipe in the coming months. For more information, contact your BBH representative and follow BBH Market Insights on LinkedIn.
1 Mercer Global Talent Trends Study, 2022
2 Pulse of the American Worker Survey, Prudential, March 2021
3 In these markets, CSDR applies penalties for trades that don’t meet the intended settlement date
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