Even with machine learning, human judgment still required in fintech sector

Even with machine learning, human judgment still required in fintech sector

A COMBINATION of recent events has seen a rapid acceleration in the adoption and incorporation of technologies by a wide range of firms and institutions in the global financial sector.

Whether this adoption has been spurred on by the global financial crisis of 2008; the need to adhere to regulation; or the immediate need to pivot and handle the consequences of Covid-19 and its impact on customers and staff, firms in the finance industry are embracing financial technologies (fintech) into their daily processes.

Designed to drive enhancement in services and improve efficiencies in back-office operations, it has seen a thriving sector developed beyond traditional ‘Wall Street’ financing.

The prospect of the part that machine learning (ML) could play is generating a lot of momentum.

The financial sector is well-placed to benefit from machine learning, with large volumes of historical structured and unstructured data to learn from. It is also open to implementing new technologies, as demonstrated by the early adoption of technologies such as algorithmic trading by investment banks in the 1980s.

Accordingly, a study by Forrester in 2019 estimates around half of financial services and insurance firms globally already use ML technologies. By using these technologies, significant and non-trivial savings have already been made. For instance, JPMorgan Chase has estimated their fraud detection solution, which uses machine learning to analyse stock market data, saves the bank $150m annually.

So, will machine learning completely automate human tasks in the finance sector? Probably not. Human judgment is still required to help with so-called ‘edge cases’, where no obvious outcome is clear, and associated decision-making.

In many ways, it represents a new synergy between human and machine. Machine learning systems can sift through enormous amounts of data and identify correlations. Human expertise is still required to tease out spurious links and noise from underlying informative signals. As highlighted by the Covid-19 pandemic, machine learning is highly capable in analysing large domain-specific data and identifying patterns to an expressed objective, but is slower to adapt to these rare ‘black swan’ events if they are not closely related to past trends.

On a positive note, using these tools alongside human judgment can improve the quality of data analysis for decision making and increase process efficiencies. Two such areas where machine learning is having an impact include fraud detection, and improvements in personalisation for customer service.

As we look ahead post-pandemic, we can expect to see the finance sector continuing to adopt machine learning technology to improve efficiencies and reduce costs across customer service, regulatory adherence, fraud detection and trading.

Machine learning strengthened with human expertise at this stage will aid in the development of more robust technology solutions.

:: Fiona Browne, head of AI at Datactics, will chair a panel discussion on artificial intelligence in financial services at AI Con this Thursday and Friday. To register for the conference go to: https://aicon2020.com/

Published at Tue, 01 Dec 2020 01:07:30 +0000

Artificial Intelligence & Machine Learning – What Do They Mean?

There was a time when we heard terms like Artificial Intelligence and Machine Learning only in sci-fi movies. But today, technological advances have brought us to a point where businesses across verticals are not only talking about, but also implementing artificial intelligence and machine learning in everyday scenarios.

AI is everywhere, from gaming stations to maintaining complex information at work. Computer Engineers and Scientists are working hard to impart intelligent behavior in the machines making them think and respond to real-time situations. AI has evolved from being a research topic to being at the early stages of enterprise adoption.

Tech giants like Google and Facebook have placed huge bets on Artificial Intelligence and Machine Learning and are already using it in their products. But this is just the beginning, over the next few years, we may see AI steadily glide into one product after another.


According to Stanford Researcher, John McCarthy, “Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. Artificial Intelligence is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”

Simply put, AI’s goal is to make computers/computer programs smart enough to imitate the human mind behaviour.

Knowledge Engineering is an essential part of AI research. Machines and programs need to have bountiful information related to the world to often act and react like human beings. AI must have access to properties, categories, objects and relations between all of them to implement knowledge engineering. AI initiates common sense, problem-solving and analytical reasoning power in machines, which is much difficult and a tedious job.

AI services can be classified into Vertical or Horizontal AI

What is Vertical AI?

These are services focus on the single job, whether that’s scheduling meeting, automating repetitive work, etc. Vertical AI Bots performs just one job for you and do it so well, that we might mistake them for a human.

What is Horizontal AI?

These services are such that they are able to handle multiple tasks. There is no single job to be done. Cortana, Siri and Alexa are some of the examples of Horizontal AI. These services work more massively as the question and answer settings, such as “What is the temperature in New York?” or “Call Alex”. They work for multiple tasks and not just for a particular task entirely.

AI is achieved by analysing how the human brain works while solving an issue and then using that analytical problem-solving techniques to build complex algorithms to perform similar tasks. AI is an automated decision-making system, which continuously learn, adapt, suggest and take actions automatically. At the core, they require algorithms which are able to learn from their experience. This is where Machine Learning comes into the picture.


Artificial Intelligence and Machine Learning are much trending and also confused terms nowadays. Machine Learning (ML) is a subset of Artificial Intelligence. ML is a science of designing and applying algorithms that are able to learn things from past cases. If some behaviour exists in past, then you may predict if or it can happen again. Means if there are no past cases then there is no prediction.

ML can be applied to solve tough issues like credit card fraud detection, enable self-driving cars and face detection and recognition. ML uses complex algorithms that constantly iterate over large data sets, analyzing the patterns in data and facilitating machines to respond different situations for which they have not been explicitly programmed. The machines learn from the history to produce reliable results. The ML algorithms use Computer Science and Statistics to predict rational outputs.

There are 3 major areas of ML:

Supervised Learning

In supervised learning, training datasets are provided to the system. Supervised learning algorithms analyse the data and produce an inferred function. The correct solution thus produced can be used for mapping new examples. Credit card fraud detection is one of the examples of Supervised Learning algorithm.


Supervised Learning and Unsupervised Learning (Reference: http://dataconomy.com/whats-the-difference-between-supervised-and-unsupervised-learning/)

Unsupervised Learning

Unsupervised Learning algorithms are much harder because the data to be fed is unclustered instead of datasets. Here the goal is to have the machine learn on its own without any supervision. The correct solution of any problem is not provided. The algorithm itself finds the patterns in the data. One of the examples of supervised learning is Recommendation engines which are there on all e-commerce sites or also on Facebook friend request suggestion mechanism.


Recommendation Engine

Reinforcement Learning

This type of Machine Learning algorithms allows software agents and machines to automatically determine the ideal behaviour within a specific context, to maximise its performance. Reinforcement learning is defined by characterising a learning problem and not by characterizing learning methods. Any method which is well suited to solve the problem, we consider it to be the reinforcement learning method. Reinforcement learning assumes that a software agent i.e. a robot, or a computer program or a bot, connect with a dynamic environment to attain a definite goal. This technique selects the action that would give expected output efficiently and rapidly.

Artificial Intelligence and Machine Learning always interests and surprises us with their innovations. AI and Ml have reached industries like Customer Service, E-commerce, Finance and where not. By 2020, 85% of the customer interactions will be managed without a human (Gartner). There are certain implications of AI and ML to incorporate data analysis like Descriptive analytics, Prescriptive analytics and Predictive analytics.

Originally published here.

Published at Tue, 01 Dec 2020 00:00:00 +0000