2017 was a busy year for regulatory compliance and technology across the globe. We witnessed countless mass data breaches, sexual misconduct claims, money laundering scandals, and of course, the Wild West that is the Blockchain. Alongside that, we continue to see significant advancements in Artificial Intelligence (AI) and Machine Learning (ML) technologies across all industries, being applied to automate business functions, gain insights into behavior patterns, and more.This year, the Banking Industry will adopt ML and AI-based automation for enhanced efficiency and data-driven decision making.

Banks were slow to adopt ML based automation in 2017, but to remain competitive in 2018 and onward, banks will have to consider  how adding AI and ML fueled technologies will impact their growth and improve the efficiency of their business processes.

Many financial institutions have been quick to experiment with AI applications in the frontend of the business, for example, to streamline and improve customer service via chatbots. In general, the value proposition is that AI can automate manual and repetitive roles but now, we are seeing AI being applied towards broader data-driven analysis and decision making.

This not only reduces costs and saves time, it also eliminates the risk associated with human prone errors. The machine is well-situated to consume large data sets while also self-learning overtime. But before even considering the tremendous opportunities to implement this technology on the backend of the business, organization leaders will need to educate themselves on how the technology actually works.

 

AI in the Enterprise

While many AI-based solutions have advanced over years, the financial industry remains suspicious of the science behind the decisions made by such technologies. Now we are seeing a shift towards increased transparency in AI-based solutions, where the science behind machine learning (ML) based decisions can be justified, tracked, and verified. This should help move along industries on the cusp of adoption.

Artificial Intelligence and machine learning in the long term can be applied to reduce costs and time by automating a once manual process.  However, on average, most AI algorithms are only about 80% accurate, which doesn’t live up to the business standards of accuracy. That leaves 20% flawed, which requires human input to bridge the gap. There is an inherent design flaw to any AI solution which does not utilize some human component in development. It is a general understanding that the most successful AI models use the 80:20 rule, where 80% is AI generated, and 20% is human input. This is implemented in the form of supervised learning or human-in-the-loop.

 

Human-in-the-loop Integration

A best practice in the successful development of AI includes a human component, typically referred to as “Human-in-the-loop” or supervised learning model. The way it works is that machine learning makes the first attempt to process the data and it assigns a confidence score on how sure the algorithm is at making that judgement.  If the confidence value is low, then it is flagged for one or many humans to help with the decision.  Once humans make the decision, their judgements are fed back into the machine learning algorithm to make it smarter. Through active learning, the intelligence of the machine is strengthened, but the quality of the training data is based on the human contributors.

(CrowdFlower Inc, n.d.)

Some data analysis is specific and complex, such as the case with Financial Regulation. The evolving and complex nature of regulation is a tough subject matter to master. AI in RegTech requires an in-depth knowledge and understanding of the regulatory framework and how to read and interpret the text.  In these types of fields, expertise is far more critical than the tool. However, if a tool could incorporate subject matter experts into the machine learning model, then the tool becomes exponentially more viable.

Expert-in-the-Loop takes Human-in-the-Loop to another level. It makes use of subject matter experts to train the machine and flag the machine’s errors. For example, a well trained machine in the RegTech industry could eliminate countless hours a compliance officer takes in researching, reading, and interpreting regulations, by automatically classifying documents into topic-specific categories or by summarizing the aspects of a document that have changed from a previous version.

The Expert-in-the-Loop model differs from Human-in-the-Loop in one major way: Human-in-the-Loop doesn’t differentiate between the aptitude level of the various participants to judge the particular question correctly. Human-in-the-Loop takes advantage of the Law of Averages which states that if many people participate, the average response will yield the correct result. So the response from a college student and a PHd student would be weighed the same. On the other hand, Expert-in-the-Loop , specifically looks at the experience level of the participant to determine how their result will be weighed.  With Expert-in-the-Loop, a human is essentially supervising another human’s qualifications. While the cost is higher than both the unsupervised and the Human-in-the-Loop models, the results of Expert-in-the-Loop models are proportionally more accurate, making them suitable for highly specialized and industry specific topics.

Nearly every industry is exploring how to use AI and machine learning as tools to increase efficiency and streamline data analysis, among other things. The future holds endless possibilities for this emerging technology. It serves as a bridge to close the gap between information and the time it takes to compile results. The speed of data can bring about a new era of understanding and increased reaction time in the Financial Services industry.  There are a lot of unknowns still left to address, but the technology is becoming more intelligent and its applications more advanced. Early adopters will have the benefit of experience on their side once the inevitable industry-wide adoption finally falls in place. Until then, organizations can pilot new applications and evaluate their impact and success. Ultimately, the financial industry will need to educate themselves on the pros and cons, while considering the implementation of this new technology.