“Big data is especially promising and differentiating for financial services companies,” argued a 2013 report by IBM entitled ‘Analytics: The real-world use of big data in financial services‘. “With no physical products to manufacture, data – the source of information – is one of arguably their most important assets. The question for many of these firms remains: how to harvest and leverage this information to gain a competitive advantage?”

Four years on, and we might finally have an answer: AI. “It all comes down to data overload. No matter the industry, everyone is creating and producing massive volumes of secure and unsecure data. The financial industry is no exception,” argues Akash Ganapthi, CEO and Co-Founder of Trill A.I. “It has an overload of data, making it a prime candidate for the advances of AI technology. Although there are hurdles, AI offers a substantive way to enhance performance and productivity, improving the bottom line for retail customers.”

The Value of AI To Financial Services

Trill A.I. are an AI-first company promising super-efficient data analysis and augmented decisionmaking in financial asset management. In Akash’s own words, “Trill A.I. has innovated in the long-term investment space with deep-learning solutions that improve performance and efficiency.”

Akash Ganapathi is a North Carolina entrepreneur and the cofounder of Trill AI, a provider of deep-learning, artificial-intelligence solutions for financial institutions and asset managers. Akash comes from a data science and algorithmic trading background. Prior to founding Trill in 2014, Akash worked at Cisco doing mobile predictive analytics and at SAS as a software engineer.
Akash Ganapathi is a North Carolina entrepreneur and the cofounder of Trill A.I., a provider of deep-learning, artificial-intelligence solutions for financial institutions and asset managers. Akash comes from a data science and algorithmic trading background. Prior to founding Trill in 2014, Akash worked at Cisco doing mobile predictive analytics and at SAS as a software engineer.

Akash believes that, given the huge volume of data produced by the financial sector, everyone in the industry is looking for a new dataset that can provide an investing edge. This, he argues, “inevitably” places a number of stressors on the teams of analysts responsible for financial data. “The amount of incoming and available data today makes it impossible for human teams alone to manage, interpret, and unify financial results well. That’s where the need for machine assistance comes into play.”

There is then a clear and long-awaited use case for AI and machine learning technologies in financial services, and this doesn’t just extend to new and unstructured data coming out of the sector. Akash holds that historical data can reap the benefits too. “Whether we are talking about banks, asset managers, or custodians, financial institutions have been storing data for decades. Although these storage systems are not always in the cleanest formats for analysis, the presence, organization, and integration of this data can become an invaluable proprietary asset – one that is further bolstered by AI and ML technologies,” he argues. “It’s the combination of applying historical data against future insights through AI and ML technology that offers the industry a competitive advantage. It’s something customers care about because the benefits are direct and tangible; impacting loan prices, investment performance, and compliance.”

Strategic Challenges: The Roadmap To AI Success

While Akash asserts that there are clear and quantifiable benefits to having an AI-assisted investment portfolio, such as improved analyst productivity, better performance, and cost savings, he admits that there are also a number of regulatory and management challenges ahead. “The regulatory environment is constantly evolving as advancements are made in AI. The challenge is to clearly articulate and justify the reasoning behind AI-based investment decisions, and ensure sufficient oversight. Doing so will ultimately drive further AI adoption within the industry and trust among regulatory bodies.”

He also notes the number of internal challenges that can emerge when it comes to planning for data structuring around AI. “It is critically important to have well-governed data, data collection, data cleaning, and pre-processing systems in place in order for AI solutions to run effectively and without error or bias,” he argues.

“It’s the combination of applying historical data against future insights through AI and ML technology that offers the industry a competitive advantage. It’s something customers care about because the benefits are direct and tangible; impacting loan prices, investment performance, and compliance.”

Data is one crucial measure of success with AI, but equally important is the need for a strong AI-supported investment strategy. For this to be effective, Akash argues it must augment decisionmaking and improve performance per cost – particularly for early and ‘deep’ adopters. The key, he claims, is that it does not replace the need for human judgement. “Rather, it improves the productivity of investment teams and allows investors to focus time and attention on areas where technology is not capable of performing well.”

“Whether integrating unique datasets, conducting bottom-up interviews among company management or factoring in other proprietary market variables, human analysis is fundamental to effective investment decision-making. Investors will need to aim for a well-designed AI system that ingests data and analyzes it in the most statistically robust way possible, and improved performance will follow if the AI is well integrated with existing human teams.”

AI Will Have Industry-Wide Implications

The widespread adoption of AI technologies across the sector carries a number of implications for the industry as a whole. Some of these, Akash argues, are great, while others raise concern. “Consider the impact on retail investors,” he says. “The adoption of AI will offer improved investment performance at lower fees. On the other side of the coin, however, implementing AI technologies may put pressure on the industry to reduce analyst jobs.”

“However, other jobs will open. While AI technologies may replace certain roles, analysts will still be needed for algorithm oversight and the analysis of data that is hard to acquire. Roles will be expanded around alternative investments, data aggregation, and risk management.”

“The widespread adoption of AI-assisted technologies within the financial services industry will be transformative. Eventually, we will see banks and asset managers that fully operate virtually. We will also see improved operational efficiencies among customer service, loan pricing, compliance reporting, marketing, and regulatory assistance.”

Trill A.I. at The AI Summit NYC

“At the Trill A.I. booth, summit attendees can expect to learn more about how deep learning and machine learning are transforming long-term asset management. Attendees will learn how A.I. can be used to enhance productivity and perform specific types of data analysis faster, more comprehensively, and with better results. Deploying the technology will free up the time of analysts, giving them the opportunity to perform more high-value and engaging work. Attendees will also learn how Trill A.I. products are augmenting current processes at a variety of financial institutions to supplement capabilities and improve performance by augmenting idea generation, security selection, and portfolio allocation.”