Banking Industry is Changing: Here’s Why

  • Posted by: Kenneth Ochieng
  • Category: Blog

 Artificial Intelligence, ML and The Future of The Banking Industry

Most Banks (80%) are always looking for ways to increase the efficiency of their operations, according to an OpenText survey of financial services professionals. New  technologies make it difficult to decide which ones are worth the investment or just a fad in the banking industry. This article looks at how artificial intelligence (AI) and machine learning techniques can help banks use today’s technology to adapt to tomorrow’s innovations.

What is Artificial Intelligence?

Often times we’re worried about what AI really is. We want to share our views very objectively. The buzz around AI has been there for more than a decade now. And several tech experts are incessantly working towards building state-of-the-art solutions. They are seeking better optimization functions, figuring out the best ways to bring out solutions.

All of this is excellent!

But here’s the time when we say we’re in a “data” world. Every millisecond of our lives is captured in some digital format or the other. This means every company which is digital in any capacity is data-driven. Because they’re capturing and storing a lot of information.

There is still quite a bit of work that you’ll need to do to understand AI.

What the hell is AI?

Artificial intelligence is a term that gets thrown around a lot these days. So, let’s take a step back and unpack what it actually is.

In its simplest form…

Artificial intelligence is a field of computer science and engineering focused on the creation of intelligent agents. These are systems that can reason, learn, and act autonomously.

So, what does that mean in practice? Well, AI can be used to develop everything (chatbots and digital assistants to self-driving cars and smart homes).

According to Us

Artificial intelligence is not just about developing new technologies. AI is also being used more and more to help businesses automate tasks, make better decisions, and improve their customer service.

For banks, AI helps to help prevent fraud, streamline processes, and personalize service for customers.

So, what does the future hold for AI? Well, it’s hard to predict exactly, but one thing is for sure – it’s already a big part of our lives.

AI applications can be deployed in a number of ways, including:

  1. Machine learning: This is a method of teaching computers to learn from data, without being explicitly programmed.
  2. Natural language processing: This involves teaching computers to understand human language and respond in a way that is natural for humans.
  3. Robotics: This involves the use of robots to carry out tasks that would otherwise be difficult or impossible for humans to do.
  4. Predictive analytics: This is a method of using artificial intelligence to make predictions about future events, trends, and behaviors.

The banking industry is one area where artificial intelligence is having a major impact. Banks are using AI applications in a variety of ways, including customer service, fraud detection, and loan approval.

What is Machine Learning?

Machine learning (ML) is a subset of artificial intelligence. It deals with the creation of algorithms that can learn and improve on their own. ML algorithms have been able to achieve some impressive results in recent years. Talk of beating humans at certain tasks such as image recognition and Go.

The banking industry is starting to make use of ML in a many ways. For example, ML is helping to detect fraud, to better understand customer behavior, and even to automate certain tasks such as customer service.

ML is not in its early days, and it’s clear that it’s also revolutionizing the banking industry.

 

Understanding AI and Machine Learning Capabilities in the Banking Industry

Banks are under constant pressure to keep up with the latest technology. They must adopt new ways of doing business or risk being left behind by their competitors. This is especially true in the area of artificial intelligence (AI) and machine learning (ML). AI and ML have a profound impact on the banking industry. But this will only increase in the future.

Financial service provider are using AI and ML to automate many tasks. This is freeing up employees to focus on more important activities that require human interaction. # personalizing the banking experience for customers.

On to the Banking Industry Report

Take the case of the Central Bank of Kenya’s latest report on the adoption of technological developments in product offerings. AI and ML continue to be the latest innovations whose developments are considered important by financial institutions. Financial institutions indicated a high likelihood of ramping up their innovation efforts towards developments in AI and ML in the next four years.

We’ve scoured the web to round up for you Artificial Intelligence and Machine Learning trends for this year. These are our predictions for what is going to be the most popular way of developing technology in the foreseeable future and what you need to know right now.

One in ten banks now uses ten or more AI and ML-based products and services to generate revenue.

MMC Ventures.

This indicates how the banking industry is slow to change and hesitant to adopt new technologies – but that is starting to shift. The use of AI and machine learning is increasing within the sector.

One area is in customer service. Banks use chatbots and virtual assistants to help customers with basic tasks such as checking account balances or providing account history. These tools free up time for human customer service representatives to handle more complex inquiries.

Another way is fraud detection. And we love what Benard Marr, a world-renowned futurist, influencer and thought leader in the field of business and technology, says about it.

The financial services sector has also been one of the keenest early adopters of AI, where its role in the automation of repetitive processes, risk assessment, and fraud prevention is well established.

Machine learning algorithms also sift through large amounts of data much faster than humans, making it easier to identify fraudulent activities. This can help banks save money by reducing the number of losses due to fraud.

AI and machine learning also personalize the user experience for bank customers. By analyzing customer data, they help banks provide customized recommendations and services that are tailored to each individual. For example, a customer who frequently overdrafts their account may be offered a different type of account with features that could help them avoid fees.

In addition, AI and ML enable banks to make better lending decisions. Banks can now analyze borrowers’ financial histories and predict their ability to repay loans. This information helps banks avoid making bad loaning decisions that could lead to defaults and losses.

Overall, the role of technology in the banking industry is changing, thanks to the rise of AI and ML. Potential applications of AI and ML in banking are endless. In the future, we will see even more amazing innovations in this field that will change the way banks operate and interact with customers.

Getting it Right from Machines

It’s a beautiful thing to see your baby start walking. If we have to summarize the power of AI and ML we’ve seen so far, we’d say we’ve all directed it towards our potential to deliver next-generation business models for growth-minded businesses.

If you’re wondering how AI/ML-based solutions look like, this is the nature of what we’ve provided our partners.

In the world of debt collection

We’re using AI and ML technologies to help lenders gather and process information useful for classifying debtors and recommending actions regularly. Something that credit management departments now use to enhance productivity or support business growth.

Just for illustrative purposes, such information may comprise:

Debtor:

  • Demographic: age, profession, education, gender, etc.
  • Financial records: income and spending, debts, mortgages, etc.

Loan conditions:

The amount of money loaned, annual percentage rate, timeline, guarantors, consequences in case of contract breach/failure to pay, etc.

We’re also Enabling Unbiased Credit Scoring Systems with AI

Our AI expert Vikram Bandaru talks of a case where we’ve removed the human element of decision making and replaced it with machines to get it right in credit scoring.

Compared to traditional credit scoring models, our AI-based credit scoring algorithms offer a more sophisticated review of the data and can take into account information that might not first appear important or even included.

AI gives rules that are more complicated and in-depth. This is unlike traditional credit scoring models that utilize relatively simple criteria and frequently reject credit-worthy applicants.

A self-learning credit scoring model also keeps getting better as fresh information is added to the system.

Conclusion

We can only imagine the new ways we will continue to transform the banking industry even more this year and the years to come as artificial intelligence and machine learning continue to evolve.

We’re definitely going to do a big launch of solutions in the pipeline once they all come together. They’ve got so many great features and lots more capabilities. We usually hate the buzz words but we do have a couple of interesting solutions already deployed.

If you would like to discuss further, what has been exposed here, feel free to contact us today.

 

/Speak to Sam or Kalya/

Tel: +254-20-263 2768.
Email: sales@nlske.com