Research Paper Covering All Aspects of AI in Banking

Artificial Intelligence (AI) has been heralded by Stephen Hawking as either humanity’s greatest or worst hope. When examining the current reality, it is clear that AI has had a profoundly positive impact on people’s lives across the world. A recent study by Forbes reveals that 70% of the financial services industry is now utilising Machine Learning to optimise their performance and increase their bottom lines. This includes areas such as credit scores and fraud detection. Additionally, 61% of workers report feeling that AI has had a beneficial effect on the efficiency of their work. It is clear that AI has already revolutionised many industries and will continue to do so in the future.

This article will examine how Artificial Intelligence (AI) has been instrumental in transforming the financial sector and the challenges that have been brought to light as a consequence. We will explore the ways AI is being utilised in banking, investments, and risk management, as well as the potential drawbacks that come with its use. Finally, we will discuss the importance of understanding the implications of AI in the financial sector and the need for continued research and development in this area.

Positive effects of AI on the financial sector

The utilisation of Artificial Intelligence (AI) has revolutionised the finance sector, streamlining the processes associated with income creation, spending, saving, investing and security. This technology has enabled finance professionals to perform complex tasks quickly and efficiently, significantly improving the efficacy of their operations in comparison to the manual methods previously used.

Some other advantages are as follows:

  • Conversational banking is aided by AI.
  • Consequently, it reduces the number of false positives and the likelihood of human mistakes.
  • It boosts productivity since fewer tasks must be performed repeatedly.
  • Effectively preventing and mitigating fraud is made easier with this tool.
  • It’s useful for data analysis since it makes it easier to draw conclusions about consumers, companies, etc.

Using Artificial Intelligence for Financial Purposes

As customer comfort with Artificial Intelligence (AI) increases, it is creating a demand for the banking sector to keep up with the times by leveraging the latest technologies. Financial institutions are incorporating AI-based algorithms into a wide range of their operations, such as customer service, fraud detection, and financial analysis. By taking advantage of the opportunities that AI offers, banks can increase their efficiency and improve customer experience.

Money management

Consumers are increasingly looking for resources to aid in their financial literacy and to gain financial independence. Natural language processing in chatbots has made it possible for them to have access to such information and assistance 24/7. Capital One’s Eno, which launched in 2017, was one of the first bank-based text-based assistants that employed the use of artificial intelligence. It provides users with the ability to be notified of any suspicious account activity or fraudulent activity.

Scores used to determine eligibility for credit

In order to provide loan funding, banks must ensure that their customers are in a sound financial position. Traditionally, financial analysts would make assessments of an individual’s creditworthiness based on their past financial records. Now, however, the use of deep, impartial neural networks can achieve the same goals as the manual assessment process.

Many companies have begun to leverage artificial intelligence (AI) to assess the creditworthiness of potential customers. This approach involves the use of sophisticated algorithms which are fed with extensive amounts of historical data, which is then used to identify patterns and glean insights from demographic information as well as to study the financial behaviours of customers (including their savings, investment, and loan repayment). Examples of companies that have successfully implemented this technology include Lendingkart, Capital Float, and Crediwatch, all of which are startup organisations.

Combating Fraud

With the proliferation of digital business and life, instances of fraud have also increased. A recent analysis conducted by McAfee revealed that cybercrime is responsible for an estimated loss of 1% of global GDP every year. Artificial Intelligence (AI) and its derivative, Machine Learning (ML), offer a reliable means of identifying fraudulent activity. By leveraging these technologies, organisations can gain an advantage in the fight against cybercrime.

The implementation of an artificial intelligence (AI) algorithm can greatly assist in the detection of fraudulent activities. By analysing historical data, the system is able to identify patterns that may indicate fraudulent behaviour. This helps to distinguish legitimate transactions from fraudulent ones. For example, AI can be used to detect scams that involve account takeover, identity theft, or card-not-present transactions. It can also be used to detect suspicious transactions that involve unusual amounts, multiple transactions, or transactions with foreign merchants. By incorporating AI into the system, this can help to mitigate the risk of fraud and ultimately prevent the occurrence of fraudulent activity.

  • Integrating Supervised and Unsupervised Models
  • Analytics of behaviour
  • Building complex models using extensive data sets
  • Artificial intelligence that can learn on its own and dynamic analytics

It is essential to bear in mind that cyber criminals are constantly utilising new technologies to perpetrate fraud, so it is necessary to regularly analyse and enhance these algorithms. If the models and algorithms are not upgraded to take into account new fraudulent activities, there is a risk that they may give inaccurate predictions.

Automatic trading systems

Time is of the essence in the trading industry; rapid decision-making and exact precision are essential for effective trading. This is because if an individual takes too long to understand the market, analyse graphs, examine trends, and recognise other patterns, they will likely miss out on valuable opportunities. By incorporating Artificial Intelligence with trading algorithms, investors can gain a competitive advantage.

Modern deep learning networks and cutting-edge machine learning algorithms are enabling the development of AI-powered algorithmic trading platforms. These systems are equipped to rapidly analyse data and make difficult decisions. As a result, both large financial institutions and individual investors are able to create their own automated trading systems. For example, RegalX and Regal Assets’s spinoff company, AI Autotrade, is creating robots that are capable of autonomously trading through a combination of technical analysis, artificial intelligence (AI), and self-learning algorithms. These robots are responsible for managing profitable deposits.

Automation of Procedures

By automating routine financial processes such as transaction processing, auditing, compliance, and data entry, organisations can reap the benefits of significant cost savings, time efficiency, and reduced labour costs. According to a study conducted by Ernst & Young, businesses may be able to save between 50 and 70 percent of their operating expenses by utilising automation to replace resource-intensive and repetitive tasks. Such cost savings can be beneficial in helping financial organisations to streamline their operations and improve their bottom line.

Interacting with customers can be automated with the use of chatbots powered by natural language processing. These chatbots can provide responses to commonly asked questions from clients. Furthermore, new accounts can be created for clients quickly, and the necessary Know Your Customer (KYC) checks can be completed in a matter of minutes.

Robotic Process Automation (RPA) has become a popular tool for major banks such as JPMorgan Chase, allowing them to effectively manage mundane yet essential activities, such as extracting data from forms and ensuring compliance with Know Your Customer (KYC) regulations. This technology has enabled banks to streamline their operations, ensuring that customer service processes are handled accurately and efficiently.

Problems caused by AI technology in the financial sector

AI has a lot of potential benefits for the financial industry, but it also comes with its share of difficulties. We’ll look at a few examples now.

Safeguarding and observing regulations

The sheer volume of data being collected, much of which is considered to be confidential, is one of the most pressing issues faced by the financial sector. If customer financial data is left vulnerable and exposed, it can have serious repercussions. Therefore, it is essential that data storage, access, and other related activities are undertaken with extreme caution and vigilance. Protecting customer financial security is of the utmost importance and must be taken seriously.

Quality of Data

The maxim “garbage in, garbage out” is particularly pertinent in the financial industry and often referenced when discussing data science. Poorly informed credit scoring decisions made by artificial intelligence algorithms can have devastating consequences for consumers, while incorrect predictions of fraudulent activity can lead to considerable financial losses. Therefore, it is of the utmost importance to ensure that all information is obtained from reliable sources.

Reduced dimensions

The complexity of the data in the financial industry is immense, with hundreds of data points. The results of any predictive modelling can vary drastically depending on the approach used to handle such a feature-rich dataset. Consequently, it is essential to conduct an analysis, select relevant features, and reduce the dimensionality of the data before it can be applied for further use.

The Prospects for Financial AI

Despite the vast potential of Artificial Intelligence (AI) in the financial sector, it still faces some considerable challenges. The objective must be to ensure secure, lawful, and uncomplicated financial transactions. The utilisation of Blockchain technology is also on the rise at present. By leveraging its security features in the financial industry, the trust between clients and financial organisations can be enhanced, making AI systems more secure and transparent.

Recent statistics from Oberlo demonstrate that the majority of leading companies have sustained investments in Artificial Intelligence (AI), and the usage of AI among organisations has seen a remarkable growth of 270% in just four years. Moreover, a survey from the same source reveals that 62% of consumers are willing to share their information with AI if it could lead to better services. These figures clearly indicate that AI is already making a profound impact and is set to continue doing so in the future.

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