In the past few years, AI has been everywhere. From self-driving cars to voice assistants, from image recognition to language translation, it is hard not to notice this trend. But there is one area where AI is likely still very much in its infancy: your company’s operations.
Fig1. Key challenges in AI Adoption
AI’s potential for improving operational efficiency and effectiveness is enormous. However, most financial markets firms are only just starting to explore it. AI requires a fundamental shift in mindset: from a world of business logic and rules to one of learning and matching inputs to outputs.
Fig 2. Surveyed companies asked about the status of AI initiatives within their firms
Financial markets are also tough to disrupt. There is a natural focus on revenue growth versus transformation, the business is highly regulated and benefits from economies of scale. Stability, track-record, assurance, safety & security are valued attributes. All these factors together make it extremely tough for new entrants.
Why should you care? Because your competitors care—and they won’t be operating in the same way tomorrow. Established firms are highly unlikely to lose market share to agile new start-ups in capital markets, despite some of the vocal claims of DeFi proponents. But they are vulnerable to losing business to mature competitors
Established benefits of AI for Financial Services include:
Automating tasks that would otherwise require human input (e.g., identifying anomalies in real time, fraud and money-laundering monitoring)
Extracting new insights from data (e.g., predicting customer churn, better risk management and insurance premia pricing)
New revenue streams (e.g., aggregating client activity across all businesses in the firm and offering bespoke services”)
Reducing the cost of operations (e.g., not simply automating repetitive back-office processes, but augmenting or replacing tasks that require cognition and decision making)
Figure 3. General Benefits of AI Adoption across all sectors
In a study of 283 firms[1] Harvard Business Review determined that increase efficiency, productivity and improved customer experience were all more important benefits than cost reduction (see Figure 3).
Fig 4. Industry Sector of firms in HBR study
How far away are we from an AI company?
As with many other areas of technology, we tend to think about AI in terms of levels or maturity—from basic research projects through early adopter stages and on towards full commercialization.
In the case of AI within Capital Markets, we are somewhere between early adopter and pilot project stage right now—with full commercialization some way off yet (see Figure 2). By ‘some way off’ we mean low units of years.
Desk level vs department wide
Why might this be? While many companies have embarked on their own prototype AI projects so far, they haven’t yet merged them all together into core systems across all departments or functions within the business—a key step towards full mainstream adoption.
Starting small helps firms build up their skill set and learn from successful and not so successful projects before taking on more ambitious, department-wide initiatives. But AI brings scale benefits. The firms that succeed will be the ones that can make the leap from desk-level prototypes to department-level integration.
Skills gap
A key challenge highlighted by the Harvard Business Review survey was in education and skills (see Figure 1). This came top of the list of challenges faced in adoption AI. Also high on the list were inadequate data, the expense required and a the challenges of creating a strategy.
Universities have been keen to equip their graduates with AI skills, but this is a recent phenomenon. Firms often have many people with a deep understanding of their business and solid subject matter expertise. They may also have hired staff with solid grounding in Machine Learning and Artificial Intelligence.
But it is rare to find staff with decades of business experience allied with a fundamental understanding of AI.
Lack of Transparency
While AI systems can often match or exceed human performance, they are often inscrutable. Their decision making often has no obvious set of rules. Simply a set of parameters in a complex equation. Much like human intelligence it can be challenging to articulate how a decision has been reached. Without greater transparency it can be difficult to trust AI systems for large-scale, mission critical tasks. Harder still in such a tightly regulated field as Capital Markets to gain approval from internal compliance and external regulators. Being able to visualise and explain the behaviour of AI is vital for successful adoption.
What does this mean for AI Adoption in the immediate term?
All these factors combined mean that we aren’t quite at the point where many Capital Markets firms can say “we are an AI company” just yet—but we aren’t far off either.
The explosion of information within Capital Markets firms is a key factor in the rise of AI. This explosive data growth makes AI both practical and vital. Harnessed effectively this information gives a competitive edge over established competition
"The quantity of information is greater in service industries than in manufacturing, so service companies are those in which differentiation is largest — and those without AI risk falling further behind" - Esteve Admiral [2]
If you want to stay ahead rather than lag behind your competitors then now is the time to start exploring how AI can help your business become even more effective and efficient over time—by extracting new insights from your data; enhancing the accuracy and speed of responses to queries; reducing costs; etc.—rather than simply relying on traditional automation methods today.
If you want further details about what kind of approach might work best for your specific needs then please feel free to get in touch via email at info@lucidate.co.uk. The future will bring...AI-powered superiority.