The roadmap to the future of Digital Transformation

Dramatic as it may sound, gaining, sustaining or losing a momentum may shape the spiral of digital transformation in the financial arena. Unstructured data being the new oil is just the tip of the iceberg and DL aggressive strategical approaches is key to unlock a new set of opportunities in the search for true value when tailoring an organization’s response to crises, changing customer behaviour, and broader market conditions.

Why is DL relevant in Finance, specifically in trading? What challenges FS are facing when implementing DL techniques? What is state of the art DL trading algorithms to deeply mine data for insight and enable the development of predictive and descriptive capabilities? What about NLP current uses and best practices to good effect? Are organizations making the most of DL techniques? Where can AI/DL truly be leveraged? How about the teams? Are traditional ML teams good enough to develop models able to achieve long lasting competitive advantage? Certainly, the answer lies in those who can envision ‘future state’ that the digital transformation aims to deliver.

It is of utmost importance, then, to understand how AI can accomplish digital transformation by securing and achieving early quick wins. Notice that historically many financial institutions started in areas where modelling has been a constant for many years such as fraud, credit risk, among others. And while these areas mean significant ROI, they are only scratching the surface of the possible applications of AI/DL within FS. An insightful holistic approach is the next step towards the best-action.

Now let’s try to solve the “puzzle” by answering some of the questions aforementioned.

NLP in banking current uses and applications

Intelligent document search, investment analysis, interpretations of ECC, Sentiment Analysis just to mention some of the many use cases. Key considerations include whether to build AI/NLP tools in-house or to license software from an AI vendor.

Banks are using NLP tools that “read between the lines” hundreds of documents in real time and summarize key information for human analysts. Document search tools can analyze feedback forms and customer information to respond to issues, offer tailored products, and increase customer retention. Most benefit from conversational analysis tools that can “listen” to analyst conference calls to determine the tone and sentiment behind what company management is “saying or not saying”.

“Skimming and scanning” vast amounts of news and social media posts to extract key insights, determine how a company is perceived, or track market reaction to significant events, to identify relevant information including unexpected news, emerging trends, or potential risks. The other side of the coin: A bigger win for banks will be using NLP to better understand and predict customer needs.

Therefore, by analyzing text and speech data more quickly and extracting more actionable insights on customers and the market, banks can serve customers better and make better investments. Finding the needle in the haystack can be a competitive difference maker.

The other dilemma: vendor tools or in-house developments. Well, this is more than a matter of “taste”. It involves a hot topic like “data privacy”, costs and timing regarding deployment.

Blending the best of two worlds for an “A team”. Academia, research projects and building the “ideal team” where interdisciplinarity is a must. Creative thinking, collaboration, proficiency, flexibility, shared vision and training are the ingredients for an elite team. The move from functional to interdisciplinary teams brings together the diverse skills and perspectives to build effective tools. In this sense, the MetaQuant has emerged as a crucial component of any DL/NLP model where hybridization is critical. The formula for a successful organization in a discovery-driven environment is the MetaQuant + The ML/DS team, and eventually the Quantum Computing Expert.

Deep Learning in Trading

The core in challenging times is the descriptive value: to identify changes in industries rather than market trends. DL technology can make it a lot easier to understand the direction of a given industry or the company itself. In perfecting a framework aimed to ascertain the optimal DL model in a specific condition, it is crucial to identify various application scenarios.

Some of the most common strategies:

1. DRL for portfolio balancing and selection of financial instruments. With the help of Deep Policy Network Reinforcement Learning, the allocation of assets can be optimized over time.
2. When it comes to online trading platforms, recommendation systems based on reinforcement learning techniques can be a gamechanger. DL tools are designed to help the user choose the best stock or mutual fund, ultimately leading to better ROI.
3. Risk optimization in peer-to-peer lending. Here RL comes in handy to analyse borrowers’ credit scores to reduce risk, predict annualized returns, help estimate the likelihood if the borrower will be able to meet his/her debt obligations.

Challenges that FS are facing when implementing DL techniques

Financial institutions face a myriad of challenges when it comes to using data to make decisions at scale, for instance: (1) the quality of the data and accessibility, (2) alignment for regulatory compliance, (3) explainability requirements, (4) hiring new talent which may, in some cases, struggle to integrate with the organization’s culture, (5) onboarding FinTech Partners often presents a major challenge since FinTechs often not being subject to the same level or degree of regulatory and risk management.

The future of digital transformation lies in the granularity, quality, and timeliness of how data is captured in a digitized process, how it is stored and interpret or “read” using the most advanced NLP/ DL techniques combined with a holistic perspective.