AI in investment banking is an immensely powerful tool for unlocking customer insight. Leon Saunders Calvert, Global Head of M&A and Capital Raising, says the benefits are only a few steps away for firms with quality data.
- AI in investment banking can strengthen opportunities in areas such as mergers and acquisitions by unlocking new customer insight.
- AI-driven insight finds commonalities among disparate datasets, empowering account managers to be more proactive with their clients.
- By filtering, tagging, and manipulating data, AI technology such as Squirro is a powerful tool when combined with high-quality data.
In the decade since the global financial crisis, investment banks have seen increased regulation and compliance burdens add to the complexity of doing business. This has led to significant shifts in product profitability and scarcer capital.
The emergence of innovative and agile start-ups offering cheaper and faster products and services is an additional challenge to investment banks, whose outdated and inflexible physical infrastructures make digitalization a difficult task.
As a result, the ability to source new deals is more important than ever. Investment banks must retain existing clients and win new ones to remain competitive.
Despite these challenges, the investment banking sector is starting to look to the future with confidence. This is due in part to its willingness to embrace and deploy new technologies such as artificial intelligence (AI).
There is astonishing customer insight contained in the data held by investment banks, though until recently much of that insight was difficult to extract.
This is changing thanks to the use of AI in investment banking, especially when it is supported by powerful, well-structured data.
How can investment banks use AI?
Investment banking has been slow to embrace new technologies, but in the past 12 to 18 months AI has emerged as a critical area for development to drive and derive competitive advantage.
But AI alone isn’t a solution — it requires high quality data to deliver results.
The common belief that a firm can overcome data-quality problems by making use of massive amounts of unstructured, disaggregated, imprecise data to reach useful conclusions has proven to be flawed.
Bringing big data and AI together involves a few steps: the co-mingling of proprietary information, connecting disparate datasets, using predictive tools, and adopting automation to support better workflows.
Banks are increasingly interested in this process.
Squirro research with global tier-one banks — ‘Enhanced Bankers – The Impact of AI’ — published in Q2 2018 revealed that 83 percent of firms have already evaluated AI and machine learning, while 67 percent have actively deployed them.
Investment banks know they have to adapt to keep pace with the market and have realized that using AI is an highly effective way of doing so.
Unlocking customer insight
Historically, customer relationship management (CRM) platforms have been unable to process unstructured data, meaning that a vast trove of insightful information remains unused, to the detriment of the firms that hold it.
AI in investment banking can help derive value from it, strengthening opportunities in areas such as mergers and acquisitions by unlocking new customer insights.
The data sits together in the same space and helps users generate real-time, actionable insights that were previously unavailable.
External market data and internal proprietary data are stored in the same place, fully secure, in the user’s CRM system.
Workflow efficiencies
With this new approach to leveraging combined data, investment banks can drive efficiencies in workflow and uncover hitherto unrevealed connections, players and opportunities.
Investment banking is a highly competitive and heavily relationship-based industry.
AI-driven insight can unearth new opportunities because of its ability to find commonalities among disparate datasets, empowering account managers to ideate proactively with their clients.
Eighty-seven percent of respondents in the Squirro research said the impact would be of high significance if an AI engine using relevant data could spot events that led to engaging with a client on a potential deal.
By identifying significant catalysts across terabytes of unstructured information, data-driven AI allows investment bankers to approach clients with confidence, opening up new growth potential.
Driving change
Embracing digitalization, against a backdrop of legacy infrastructure and a naturally conservative outlook, is an ongoing challenge for investment banks.
Many influencers cite the need to embed digital throughout an organization’s DNA for it to be truly effective. But this does not mean that tangible benefits cannot be wrought with relative ease.
AI, coupled with powerful data, can make this happen.
It takes computing power and puts it in the hands of business users, not just the technology teams, who can deploy data analytics tools to enhance their value propositions.
In an AI-driven world, digital becomes an integral part of day-to-day operations, instead of a bolt-on or something kept in a silo and used only by a select few. Deploying it on a CRM system is logical, intuitive, and user-friendly.
Quality data and AI
Investment banking is starting to take AI seriously.
By filtering, tagging, and manipulating data, AI technology such as Squirro’s will prove integral to unlocking the customer insights that are crucial for investment bankers to survive and thrive in 2018 and beyond.
AI in investment banking is an immensely powerful tool when combined with the right data. It will open up brand new horizons.
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