Understanding Machine Learning and Artificial Intelligence and their effects on Financial Systems

Machine-based intelligence is streamlining and simplifying the way we do things, and though a human is the superior species, it appears artificial intelligence will soon take all the control over human actions in different industries. This is propelled by machine learning technologies which are simplifying the way we advertise, analyze data, make purchases, and how we keep ourselves safe.

But, do you understand what artificial intelligence and machine learning refer to or how they hold power to change our lives? Where do these two technologies meet?

What is artificial intelligence

The intelligent systems are either general or narrow AI systems.

Virtual voice assistants that can recognize voices and images like Amazon’s Alexa, Window’s Cortana and Apple’s Siri are enhancing user’s shopping experienced, and this is because the intelligent systems can carry out tasks without having to go through explicit programming to carry out the said tasks. This is the exact definition of narrow AI. It’s a machine learning technology evident in speech plus language recognition that operate in Siri as well as the vision-recognition systems in the self-driving cars.

Using narrow AI, you can interpret live video feeds from flying drones as they inspect infrastructure visually. These systems can also coordinate with other AI systems.

On the other end, you have the general AI which is an adaptable intellect that’s found in humans. It’s flexible and common in movies.

What is machine learning

This is what makes these intelligent systems work, surpassing your expectations. Machine learning, also called ML complements AI, but ML is more about computer systems that are fed large batches of data. The machine then learns to use the data provided to carry out different tasks like understanding speech or even creating captions for photographs. But, these processes don’t take place in a vacuum: it depends on neural networks in its learning process.

The neural networks are brain-inspired networks that consist of interconnected layers of algorithms referred to neurons. The neurons feed data into each other. Being trainable, the neurons can learn to perform different tasks through the modification of the importance that’s attributed to the input of data while the data passes through different layers of neurons. During the training process, weights attached to different inputs are continuously varied until the output is close to the desired product.

From machine learning, you have deep learning: it’s more detailed, and the neural pathways get expanded into sprawling networks with many layers that are further trained using more data.

Wondering why are we looking at the technicalities of machine learning and artificial intelligence? Well, it turns out that the deep neural networks are the systems that fuel the current leaps in technology to the extent that computers can take on human roles.

Now that we understand what machine learning and artificial intelligence, how do these technologies affect our lives especially the financial scene?

The major applications of AI and ML in the financial services industry

Artificial intelligence and machine learning are disrupting financial services across the globe and on a rather massive scale. This is seen in the increasing number of banks turning to the AI-powered technologies for enhanced processing and the development of new technologies.

With PWC’s recent study showing that AI has a potential of running $15.7 trillion worth of the economy by 2030, it’s clear that multiple industries have immense opportunities for growth.

And, with more than 200 top financial institutions attending the 2016 Machine Learning Fintech Conference, investments in AI and ML is a wise move for every business. Also, major banks are pushing their investments in AI and machine leaning increasing competition for the goodies that come out of AI.

 

But, why the increase in demand for ML and AI?

Applied properly, machine learning improves a business’ bottom line significantly. The machines catch and prevent expensive errors, improve the efficiency of service delivery, improves customer services and experience, and it also augments decision making.

While the initial investment makes some companies shy, the results pay off. It would, therefore, appear that machine learning and artificial intelligence are more than buzzwords since the algorithms used are changing how we do things. These technologies also tap into the customer’s need for convenience.

So, wherein the financial ecosystem is artificial intelligence/ machine learning supercharging?

  1. Fraud Prevention

If there were something that would curtail the success of e-commerce, fraud would top that list. And as it stands, most companies suffer losses because of their inability to manage fraud. Since financial service providers owe their clients protection against fraudulent activities, it makes sense to embrace a technology that could prevent fraud considerably. It’s also important since fraud is reported to cost Americans over $50 billion annually.

Thanks to AI and machine learning systems, fraud can be thwarted and customers’ data protected from hackers. Through machine learning algorithms, systems can compare current transactions against a user’s history to detect whether the current activity is fraudulent or not. The algorithms raise flags if there are out-of-state purchases, mismatched details, or large purchases.

The best part is that the algorithm works faster than humans, detecting the smallest detail that appears off. It can also determine if a transaction is something the account owner would process or not: all thanks to the data analysis programs that it runs and its ability to learn more about human behavior.

Amazon, Google, IBM, and Microsoft are already integrating the machine learning capabilities in their developed interfaces based online.

Your company’s lawyers will also know whether to award money to a plaintiff or not since the technology equips slip and fall lawyer Toronto with information about the cases and even the behavior of the accuser.

  1. Better Security Systems

The age of passwords, usernames and security questions we all forget after closing the active tabs is about to come to an end thanks to machine learning and artificial intelligence technologies. The new technologies will not only simply access and online security and personal security but it will also improve security in finance and banking institutions. The security will be increased by facial recognition systems, biometrics data and voice recognition. These solutions will enhance the efficiency of detecting anomalies.

  1. Insurance

Most insurance executives bet on the fact that AI is a disruptive force in the economy. And looking at insurance industry’s inner workings, these technologies have numerous applications especially because the industry is data-driven: AI and ML’s forte.

With insurers for ways of understanding their clients’ lifestyles, education levels, and health statuses before they make assessments, the algorithms in AI take the hard work out of the analysis thanks to their modeling abilities. At the same time, the development of IoTs have exploded data points thanks to wearables, and so, insurers can finally access newer formulas to help them serve their customers better.

AI algorithms also help simplify back-end and front-end processes in claim settlements while simplifying the process for calculating premiums for customers.

  1. Wealth and Asset Management

Do you understand which securities or assets promise the highest yield? While your investment advisor will not have every little detail, AI helps fund and wealth managers understand the asset and wealth management better, giving them a competitive advantage.

Thanks to the data obtained from the algorithms, you can customize the advice given to different clients. As long as your projections are correct, AI gives you close to full certainty; you can build a bigger base of clientele.

The data gathered is also helpful in understanding the market trends, as well as making better and more accurate calculations on the performance of different asset classes.

Investment predictions are now popular more than ever since the predictive algorithms let you know the stock price that you should place an order or when you should sell the stocks. The systems can be automated making them ideal for use by the small and the large investors. The best part is that the systems can give recommendations thanks to their automated analysis of the market trends.

Predictive investments are gaining ground in hedge funds where the fund managers use the algorithms to predict market trends for the future. Machine learning technologies are also expected to disrupt investment banking industries hence the development of automated investment advisors.

  1. Risk Management

Machine learning technology is important for improved risk management. Wondering why? Well, the traditional software applications will only predict a borrower’s creditworthiness using static information obtained from loan applications and information from different financial reports. However, machine learning goes a step further to identify and analyze the actual financial status of the applicant because existing market trends and news could have modified the financial reports submitted.

So, through the application of predictive analysis to make more data available in real time, the use of machine learning algorithms helps to detect any rogue investors by working across several accounts. A human investment manager cannot collect or even analyze all that data in real time.

It is, therefore, clear that the use of machine learning technologies enhances operational efficiency and in the process, increase the productivity of the investment managers while saving companies and individuals losses.

Machine learning and artificial intelligence could also be used in credit scoring by analyzing users’ digital footprints to gather information necessary when making decisions on lending.

  1. Digital Assistants

With all the items on your to-do list, a sound management system is critical. To boost your productivity, a digital assistant is crucial. Microsoft’s Cortana, Apple’s Siri and even Google’s Allo are the best digital assistants working as the best secretaries as well. Though they target different markets, these helpers remind you to run errands, visit the dentist to calling your investors.

These helpers use the machine learning technologies, and they’re designed to perform tasks like speech recognition, accessing big data, analyze data, and to perform behavior and pattern recognition. These assistants can also integrate with email and social media as well as other third-party applications.  Investing in the assistants will increase your ROI substantially.

  1. Customer Service

Customer service is at the heart of financing, and when done incorrectly or in a displeasing manner, it sends away more clients, and so, companies should work on improving their customer services delivery.

To turn things around to be the company that offers good customer service, it’s important to embrace machine learning. Wondering why this should happen when customers were introduced to automated customer service systems after they complained about slow service delivery? Well, it would appear that the current automated are not human enough even though they direct customers and give them options for resolving problems, one of which is then automated interface so that customers don’t have to wait for someone on the phone or have any form of human interaction. Don’t we all wish to be understood by other humans?

Unfortunately, these systems don’t work too well especially when there is a miscommunication: and this is the case with the voice recognition systems too. We can all count the number of times Siri, Alexa or Cortana couldn’t understand the order we placed at the store, and we had to visit the store of the bank, right.

Even with these setbacks, it would appear that machine learning offers reliable solutions in customer service delivery. This works better than the automated systems because it accesses data, recognizes the patterns of a customer, and then they interpret the behavior of customers: in short, machine learning systems understand us. As a result, the feedback and solutions offered are exactly what we are looking for because the systems understand and then respond to the uncommon requests.

With this in mind, machine learning can be used to create online and phone support systems that mimic human agents. This reduces blowbacks.

And that’s not all: machine learning is also helpful when it comes to making product selections because it weighs down your past actions against the current data provided and it can make service and product recommendations. This saves time. You’ll also like it because it takes into account the unique needs of customers so no more generic information.

  1. Network Security

The security systems set up for networks may not be as robust as expected and even though they are expected to be the best, they may not be able to detect all forms of breaches and malicious patterns. Machine learning technology, on the other hand, is more reliable as it uses intelligence analysis of patterns combined with excellent big data capabilities. It is, therefore, preferable to the traditional processes.

And with cyber attacks rendering the safest sectors unsafe, it only seems prudent to take on a path such as the use of machine learning for network security. Big companies like Microsoft are now investing in deterministic machine learning which uses statistical analysis and historical data to detect abnormal behaviors.  The only challenge is that employing machine learning for network security is an expensive step.

  1. Robotic Process Automation

Machine learning is also applied in robotic process automaton which simplifies tasks done by humans to be performed by machines. These roles include billing, underwriting support or deposit processing.

Lastly, these technologies can be adopted in marketing as it offers predictive analysis, helps with dynamic pricing, retargeting, and it also integrates the use of chatbots.

Author: Chakraborty

Dr Chakrabarty is the Chief Innovation Officer of IntuiComp TeraScience. Earlier she was Assistant Professor of Delhi University, a QS ranked university in India. Before that she has held research positions in IIT Mumbai, IIT Chennai and IISc Bangalore. She holds 2 patents and over 20 research publications in her name which are highly cited. Her area of research is in smart technologies, integrated devices and communications. She also has a penchant for blogging and is an editor of Business Fundas.