It is 2021 and there are many disruptive technologies out there that are transforming industries around the world. We have been observing cutting-edge application development and transformation specifically in the banking and finance sector. I’m going to share how Big data and Artificial Intelligence are shaping the financial technology sector with innovative and robust financial software solutions.
Technological evolution brings new opportunities for business as well as risks. But companies should embrace new ideas and keep innovating amidst constantly changing business conditions to set new standards by offering better products and services.
1. What can AI and big data do?
Artificial intelligence and Big data are emerging technologies. If you integrate them into your system, they can help you achieve the following:
- Process new data that you did not have access to or couldn’t process before.
- Process data in ways that you weren’t able to before.
Through automation, companies are now able to sort and analyze more texts, images, and spoken language. All thanks to recent advancements made in Natural Language Processing (NLP). AI programs in this area have started to outperform an average human being.
So, it is concluded that most of the repetitive and mundane tasks like transcription and more will be handled by AI programs. Big data has pretty much evolved from these new advanced functions only. The AI targets unstructured data like social media posts, depersonalized credit card transactions, satellite images, and more. Such data is rarely used by analysts. So, this new data creates another frontier in investment management.
With the emergence of technologies like machine learning and deep learning, companies could be able to find more accurate insights from the data. Nowadays, data analysis is run through linear programming techniques by placing constraints on variables and their assumed relationships. But they are not capable of removing these barriers. Although, in most cases, the barriers are removed with the help of machine learning and deep learning technologies.
2. Artificial intelligence is changing everything
This is the time of the smart generation. Everything around people is transformed into the smart and self-teaching machine and that includes every aspect of business too. But to become smart, to become self-taught, these machines need to feed on information – lots and lots of information. That fuel is also made available thanks to the internet and sensor-enabled devices like smartphones and cameras.
Now AI would consume all of this data and will learn from it faster and more accurately than any human brain could do. The concept of AI is decades old. It is just a thinking machine but what makes it different is the mass amount of data generated through various sources like the internet, computing technology, and more which has created a perfect storm of circumstances leading to the development of AI approaches like deep learning.
Deep learning is just a branch of artificial intelligence that tries to decode the data through a mechanism called an artificial neural network. This network is a set of computer algorithms that are specifically built to simulate various functions of the human brain like data sorting and decision making.
Deep neural nets are the neural networks used in deep learning. Though they are very far from being as complicated as a human brain, still they are highly complex because they are made from various layers of decision-making. However, these neural nets are proven to be faster in operating with minimal risks for errors.
From detecting frauds to customer services and operation management, there isn’t any area of the business in the finance industry left that AI couldn’t take care of. AI has implications for all of it. But it also comes with its own set of problems – ethical implications and the effects of AI on human jobs. Because if the Ai keeps growing at the rate at which it is making advancements today, it is predicted that half of the finance sector’s jobs will be replaced by artificial intelligence soon.
3. Big Data Fintech Startups/ disruptors
Innovation brings opportunities and opportunities bring challenges. And one challenge that the finance service industry is facing because of constant innovation in the domain is the steep rise in the number of disruptive startups. Leveraging all-new data-driven technology and its execution through agile methodologies, these new emerging businesses have become successful in earning a customer base for themselves. When it comes to customer services, convenience and value, customers seem to think that a less established innovative business could raise the bar in comparison to the traditional service providers.
These disruptive startups could be anyone. For instance, it could be a bank that likes to manage its financial operations through smartphone applications and websites instead of working in a good old way in those high street branches. Such new practices can help banks reduce the overheads which enables them to pass on savings to the customers through lowering the fees. A data-driven business model helps you make better and more efficient decisions especially in the cases of loans and investments.
For customers, financial services mean verifying whether the transactions are fraudulent or not, reviewing recent transactions, making instant purchases, and transferring money to friends or family with just a few finger taps at any time.
The growing popularity of such innovative technologies is used by fintech startups to their advantage much more than traditional banks and lenders.
4. How Big Data enables superior customer service
Figuring out what are the customer requirements and offering a product and services that fit our needs is no small task. Nowadays banks and other finance companies can achieve this feat with the help of big data.
Normally, when you open a bank account or take out a loan then it would mean that you will be subjected to a massive marketing blast aimed at encouraging you to sign up for every finance service and product that exists under the sun.
Banks have now started using big data that they procured from the customers to anticipate which one of their products and services will be truly useful to them at the right time. This also helps them in cutting down the expenditures from the offers that the customer is probably never going to accept.
Implying this strategy also increases customer satisfaction because from now on they won’t be bothered with unwanted advertisements. Some would have gone at length to say that this also helps the environment as the banks no longer post the bank statements in the irrelevant promotional flyers.
Fintech companies collaborate with the banks to put data-driven technologies in the hand of customers by offering them finance applications. Many fintech startups nowadays are using a strategic approach to technology to distinguish them from traditional competitors to offer financial tools and insights. The apps also alert the customers if they are accidentally overcharged or double charged on their accounts.
Banks leverage fintech software to assess the customer’s spending patterns through machine learning technology and make predictions for whether they are likely to exceed their credit limit before they will get their next paycheck or make unexpected purchases and put the banks into trouble.
5. What now?
The things we discussed in this article about how big data and AI impact the finance sector are just the tip of the iceberg. And it is only going to grow in the upcoming years. Traditional financial services including banks, investment managers, insurers, and brokerages are now infringed from both sides. Tech giants from the above are advancing with their innovative payment services, and money transfer mechanisms, and nimble fintech startups from the below.
Emerging technologies are transforming the landscape of the finance sector very rapidly more than any other industry outside of retail and marketing. If the finance companies need to stay on top or even stay afloat, they need to have to invest in the latest technology initiatives to understand their customer better, predict their behavior and drive change in the financial operations.