Impact of Big Data and AI in Fintech: Review in 2024

If you refer to the pages of history, then you will be amazed at how much technology has affected every corner of human civilization. The shift from human labor to technology usage has been subtle and smooth.

In this shift, big data and artificial intelligence (AI) have been key game changers, especially in the fintech sector. With the escalation of digital assets, most companies big or small have implemented data analytics and AI for seamless working in day-to-day businesses.

Implementation of such technology-based solutions has simplified processes ranging from acquiring customers to improving customer services. Government and private institutions nowadays rely heavily on big data and AI for performing tasks like trading, insurance, banking, and risk management.

Now let us understand in detail the concept of big data and AI and how they have affected and shaped the economic course of the world.

Impact of Big Data and AI in Fintech: Review in 2023

The convergence of Big Data and Artificial Intelligence (AI) has brought a seismic transformation to the financial technology (Fintech) landscape. As we delve into the impact of Big Data and AI in Fintech in 2023, it becomes evident that these technologies are revolutionizing financial services, enhancing customer experiences, and reshaping credit scoring methodologies. Read more on Data Science & Analytics and Big Data and Hadoop: Revolutionizing Data Processing and Analytics. 

One of the most significant impacts of AI in Fintech is evident in the transformation of credit scoring processes. Traditional credit scores relied on limited data points, leading to biased outcomes. However, AI-driven credit scoring models have revolutionized the process by considering a vast array of data. This includes transaction history, social media activity, and alternative data sources, providing a holistic view of an individual’s creditworthiness. As a result, credit decisions are now more accurate, inclusive, and tailored to individual circumstances.

The role of AI in Fintech extends to enhancing customer experiences through personalized services. By analyzing vast customer data, financial institutions can offer tailored product recommendations, personalized investment strategies, and real-time customer support. This personalization fosters greater customer engagement, loyalty, and satisfaction, ultimately driving business growth.

AI and Big Data are indispensable tools in managing financial risks and detecting fraudulent activities. The real-time analysis of data enables financial institutions to identify unusual patterns and potential risks promptly. AI algorithms leverage historical transaction data and customer behavior patterns to detect suspicious activities, mitigating the impact of financial fraud and ensuring robust security measures.

AI and Big Data have significantly improved operational efficiency in Fintech. Automation of routine tasks, reduces manual efforts and accelerates processing time. This streamlined workflow enhances productivity, reduces costs, and allows organizations to focus on strategic initiatives.

The dynamic regulatory landscape poses challenges for financial institutions. AI-driven solutions aid in monitoring regulatory changes, ensuring adherence to compliance requirements, and automating reporting processes. These capabilities help institutions navigate complex regulatory environments efficiently and maintain compliance standards effectively.

What is Big Data?

A complex collection of structured or unstructured data that is huge in volume and ever-growing is known as big data. They can be mined for information and used in machine learning and predictive learning models.

Big Data refers to the colossal volume of structured and unstructured data generated daily. In Fintech, this data encompasses customer information, financial transactions, market trends, and more. Harnessing the power of Big Data requires sophisticated tools and algorithms to analyze and extract valuable insights. Explore more on Benefits of FinTech Careers for Graduates and Top 10 Uses of AI and Machine Learning in FinTech.

The size and complexity of the data have made the traditional methods of data mining mostly ineffective. Read on to know more…

History of Big Data

The concept of large sets of data was already there in the 1970s when the creation of data centers and databases was just beginning. But, big data got a real boost when Doug Laney, an analyst of Meta Group Inc., first indicated its characters with three Vs in 2001.

The three Vs that are important for big data are: 

Volume

Large volumes of data are being gathered and stored by companies from transactions, videos, images, smart devices, social sites, and many more. For a few, it can be stored in terabytes for others the same amount can be done in petabytes. In earlier times, storing data was costly but with the implementation of data lakes and clouds, it has become a lot cheaper.

Velocity

 The speed at which data packets are received and acted upon is determined by velocity. With the growth and implementation of Internet of Things (IoT), tons of data are being handled in real time or near real-time manner.

Variety

Due to the variety of texts, images, audio, videos, and many such things, big data can come in several forms ranging from structured to unstructured data.

Recently, two more Vs have been added to this model. And, they are: 

Value

Data has become one of the most important and valuable digital assets for companies. If it does not have any intrinsic value or the companies are unable to discover that, then it becomes totally useless.

Veracity

Data is received from a variety of sources and until and unless they are being linked, matched, cleansed, and transformed, they will not be able to be used in businesses.

Usage of big data in multiple sectors

1. Transporters and manufacturers use big sets of data to optimize and manage the supply chain and transportation routes.
2. Crime prevention, emergency responses, smart city building, and other government initiatives rely heavily on big data.
3. From identifying valuable drilling sites to monitoring pipeline operations, the energy industry like fintech is massively using big data sets.

Applications of Big Data Across Diverse Sectors

Big Data’s influence extends beyond Fintech, finding applications in various sectors, including healthcare, retail, transportation, and more. In the financial sector, Big Data empowers institutions to analyze customer behavior, optimize trading strategies, and detect fraudulent activities.

What is AI?

Artificial intelligence (AI) is the method of providing machines with the capability to perform certain functions which require intelligence shown by animals and human beings. It can help machines to rationalize and solve problems to achieve a given goal. 

The term ‘AI’ was first coined by John McCarthy in 1956 at the first AI conference. A major breakthrough came in 1969 with Shaky the robot who could reason its own actions. In 2002, a robotic vacuum cleaner was created which was even a commercial success. 

And, standing today, we can see that the possibilities of AI are endless. It can be divided into four types:

Reactive Machines

These are a basic variety of AI models which are created on an algorithm to display output based on the given input data. They do not have the ability to go beyond a set of rules. The best example is a chess game between a robot and a human.

Limited Memory AI

These AI models are based on acquiring data and updating it continuously. It can recall the past acquired data for reference but the memory and updating power are limited as the name suggests. Suggestions of nearby restaurants are often done using this AI.

Theory of Mind AI

From understanding to learning emotions, many tasks can be seamlessly performed by this AI model. They can even fool anyone by passing the Turing Test but they are not self-aware. In advanced chatbots, this kind of technology is used.

Self Aware AI

This is quite a debatable topic in the world of technology, as one section believes that AI will become aware of its own existence while the other believes it is nothing more than science fiction.

Role of AI in different sectors

AI is playing a significant role in multiple sectors to improve performance and customer experience. Some of them are:

1. In healthcare, AI is being used for making assistance in diagnoses. It can almost appropriately scan for abnormalities in the human body and track a patient’s symptoms and vitals.
2. Online customer support and voice messaging systems heavily depend on the usage of AI for serving their purpose.
3. Fusing with machine learning algorithms, AI has become a vital organ in battling cybersecurity threats.
4. Virtual assistants like Siri, Alexa, and Cortana all use AI to perform functions based on the commands made.

Implementation of big data and AI in Fintech

Now as we have covered the basic blocks of big data and AI, let us understand how they have been implemented in the financial sector to reap benefits and their shortcomings.

Credit Scoring

Credit card companies’ business has boomed in modern times due to the implementation of AI and big data in their business model. Earlier, it was impossible for employees to go through the vast amount of data of an applicant for a credit card. 

But, now with the implementation of AI and big data, not only the quantitative data from banks but also the qualitative data like behavior, ability, and willingness to pay back can be accessed by these companies.

From mining information to spotting fraud and creating a risk assessment, all can be done within a short span of time. This in turn has led to an increase in SME financing, P2P lending, and neo banking.

Marketing and customer retention

We all love cashback, discounts on products, or any kind of reward. Big data with AI has simplified this by tracking customer behavior and rewarding those who make timely payments. 

One of the finest initiatives of fintech includes AI analyzing the historical transactions data sets and providing tailor-made rewards to every customer. Card-linked offers, brand affiliations, and loyalty programs have been solely created by companies for customer retention.

Customer service

Countless people are shifting to digital payments for their hassle-free nature which includes both buyers and sellers. Financial institutions have made a revolution with AI and big data which plays a key role in transactions happening in real-time.

With big data and AI financial institutions track the process from the request being made to the transaction being processed. Merchants can also track the relevant parameters like the location or the user of the card and authorize their payments based on that.

Investment management

Stock market failures and many people losing millions in that are not new. Yes, the risk has not been totally obliterated but it has been minimized with the usage of big data and AI.

Financial institutions and investment firms use big data and AI to check all the aspects of the investing object starting from its historical performance to the upcoming trends and try to analyze the performance it will have in the future.

Doing such in-depth research can flag market anomalies and minimize the risk of investment failures.

Fraud detection and prevention

AI with its self-learning capability and the pool of big data it has on its reach has been the key protector in fighting fraud crimes. AI algorithms have been written to study the patterns and behaviors of every customer. 

 

If even a simple irregularity happens, AI flags it and alerts the companies of potential risks. 

 

Disadvantages of Big Data and AI in Fintech

• Expensive and exclusive
  All financial institutions do not have the economic power to implement big data and AI seamlessly in their business model. AI also has some limitations and biases. Disabled people who are blind, deaf, or unable to use technologies sometimes get canceled out from AI services.

• Customer value and data privacy
  Finding the right balance between asking the customer for providing bits of information without hampering their privacy can be extremely tricky. And without this, neither big data nor AI will be able to perform the desired functions.

• Lack of management
  If anyone wants to open accounts in multiple financial institutions, then they have to provide KYC in all of them. Most financial institutions do not communicate with each other for certain laws and this bars them from using AI and big data.

Conclusion

Many scientists and analysts believe that we only have scratched the surface of the possibilities that AI and big data have to offer. The beauty of technology is that it is always evolving and can be molded according to the needs of an individual.

For a budding data professional, it is important to understand the core concepts and mechanism of AI, that are also driving its development and growth. The Hero Vired Integrated Program in Data Science, Machine Learning, and Artificial Intelligence is a great way to do that.