Renowned scientist, Stephen Hawking, has declared that Artificial Intelligence (AI) could either be the greatest hope or the greatest threat to humanity. Nevertheless, the current reality shows that AI has brought a significant improvements to people’s lives worldwide. According to a recent Forbes study, 70% of financial institutions are using Machine Learning to boost their performance and profits, which includes fine-tuning credit scores and detecting fraudulent activities. Furthermore, 61% of employees surveyed believed that AI has augmented their work productivity. Clearly, AI has disrupted many industries for the better and will continue to make significant strides in the future.
The purpose of this article is to investigate how Artificial Intelligence (AI) has significantly reshaped the finance industry and the obstacles revealed along the way. This article delves into how AI is employed in banking, investments, and risk management; as well as the possible disadvantages associated with its implementation. Lastly, this article stresses the importance of comprehending the impact of AI in finance and the urgency for ongoing research and innovation in this field.
Favourable Impacts of AI on the Finance Industry
Undoubtedly, utilising Artificial Intelligence (AI) has transformed the finance industry by simplifying the procedures related to generating revenue, spending, saving, investing, and security. This advanced technology has allowed financial experts to execute complicated tasks with speed and efficiency, greatly enhancing their overall effectiveness compared to conventional manual methods used in the past.
Other benefits include:
- AI assists in conversational banking.
- As a result, it minimises false positives and reduces the probability of human errors.
- It improves productivity by reducing the need for repetitive tasks.
- This tool effectively aids in preventing and mitigating fraud.
- It is valuable in data analysis as it simplifies the process of deriving conclusions about consumers, companies, etc.
Employing Artificial Intelligence for Financial Activities
As clients become more accustomed to Artificial Intelligence (AI), the banking industry is under increasing pressure to incorporate the latest technologies to remain relevant. Financial institutions are integrating AI-based algorithms into various operations, including customer service, fraud detection, and financial analysis. By capitalising on the advantages that AI provides, banks can increase their productivity and enhance customer satisfaction.
More and more consumers are seeking resources to improve their financial literacy and achieve financial independence. Chatbots’ natural language processing capabilities now make it possible for individuals to access such information and support 24/7. Capital One’s Eno, which was launched in 2017, was among the first bank-based text-based assistants to utilise artificial intelligence. It enables users to receive alerts about any suspicious or fraudulent activity on their account.
Credit Eligibility Scores
To grant loan financing, banks must verify that their clients are financially stable. In the past, financial analysts would evaluate an individual’s creditworthiness based on their previous financial records. However, the use of deep, unbiased neural networks can now accomplish the same objectives previously achieved through manual assessments.
Numerous firms are now using artificial intelligence (AI) to determine the creditworthiness of prospective clients. This method involves employing advanced algorithms to process substantial volumes of historical data, enabling them to recognise trends, draw insights from demographics, and analyse customers’ financial activities, such as investing, savings, and loan repayment. Several startup firms like Lendingkart, Capital Float, and Crediwatch have succeeded in implementing this technology.
As digital business and activities become more widespread, occurrences of fraud have surged. According to a recent McAfee report, cybercrime results in an approximately 1% global GDP loss annually. Artificial Intelligence (AI) and its variation, Machine Learning (ML), provide a dependable approach to detecting fraudulent activity. Employing these technologies gives companies a competitive edge in their efforts to combat cybercrime.
Adopting an artificial intelligence (AI) algorithm can considerably aid in identifying fraudulent activities. By scrutinising past data, the system can detect patterns that could suggest fraudulent behaviour. This capability helps to differentiate between authentic and deceitful transactions. For instance, AI can help uncover scams involving identity theft, account takeover, or card-not-present transactions. It can also identify unusual transactions, transactions with foreign merchants, and multiple transactions, all of which may be suspicious. By integrating AI into the system, the chances of fraud are reduced, thereby preventing its occurrence.
- Incorporating Supervised and Unsupervised Models
- Behavioural Analytics
- Developing Advanced Models Utilizing Comprehensive Data Sets
- Self-learning Artificial Intelligence and Adaptive Analytics
It’s important to recognise that cyber attackers are continuously using emerging technologies to carry out fraud. As a result, it’s crucial to routinely test and improve these algorithms. Neglecting to update the models and algorithms with the latest fraudulent activities could result in unreliable predictions.
Automated Trading Systems
Timing is critical in the trading sector where swift decision-making and precise accuracy are vital for profitable results. Delayed analysis of market conditions, graphs, trends, and patterns could lead to missed opportunities. Investors can gain a competitive edge by merging Artificial Intelligence with trading algorithms.
Progressive deep learning networks and advanced machine learning algorithms are driving the evolution of AI-driven algorithmic trading platforms. These systems are adept at swiftly analysing data and making complex decisions. Consequently, both financial institutions and individual investors can establish their own automated trading systems. For instance, RegalX and Regal Assets’s subsidiary, AI Autotrade, is developing robots that can independently trade through a combination of technical analysis, artificial intelligence (AI), and self-learning algorithms. These robots are responsible for managing profitable deposits.
Automating regular financial processes such as transaction processing, auditing, compliance, and data entry can result in notable savings, time efficiency, and reduced labour costs for organisations. Ernst & Young conducted a study revealing that implementing automation to replace resource-intensive and repetitive duties can save businesses between 50 and 70 percent of their operating expenses. These cost savings can assist financial organisations in optimising their operations and enhancing their financial performance.
Chatbots, which utilise natural language processing, can automate customer interactions effortlessly. They can offer answers to frequently asked questions from customers. Additionally, new accounts can be rapidly established for clients, and the essential Know Your Customer (KYC) checks can be completed within minutes.
Robotic Process Automation (RPA) has emerged as a favoured tool among major banks, including JPMorgan Chase, for managing routine yet vital tasks like extracting data from forms and adhering to Know Your Customer (KYC) guidelines. Banks have been able to optimise their operations and improve customer service processes by implementing this technology.
Issues Arising from AI Technology in the Financial Sector
There are many possible advantages of AI for the financial sector, but it also presents some challenges. Let’s explore some examples.
Compliance and Regulatory Oversight
The financial sector is confronted with a significant challenge in the form of the large amount of data it manages, much of which is private. In the event of customer financial data being compromised and exposed, there can be severe consequences. Hence, it is crucial to handle data storage, access, and other associated activities with extreme care and vigilance. The protection of customer financial security is of paramount importance and must be given top priority.
The saying “garbage in, garbage out” has particular relevance in the financial sector and is frequently cited within discussions on data science. Artificial intelligence algorithms making poorly informed credit scoring decisions can have devastating consequences for consumers, while inaccurate predictions of fraudulent activity can result in substantial financial losses. Consequently, it is essential to ensure that all data is sourced from reliable and trustworthy sources.
The financial sector has access to extensive and intricate datasets, often with numerous data points. The predictability of any modelling outcome can differ significantly based on how a feature-rich dataset is processed. It is thus critical to perform an analysis, select essential features, and limit the dimensionality of the data before it can be put to use.
The Future of AI in Finance
Although Artificial Intelligence (AI) holds immense promise for the financial sector, there are still significant obstacles to overcome. The key focus should be on facilitating secure, compliant, and straightforward financial transactions. Presently, the use of Blockchain technology is growing in popularity, with its security features being harnessed in finance to build trust between clients and financial institutions, thus bolstering the security and transparency of AI systems.
Oberlo’s recent statistical data shows that most prominent companies continue to make substantial investments in Artificial Intelligence (AI), and the incorporation of AI among organizations has increased significantly by 270% in just four years. Additionally, Oberlo’s survey indicates that a majority (62%) of consumers are amenable to sharing their information with AI if it can result in better services. These figures serve as proof that AI has already created a significant impact and is poised to continue revolutionising the sector in the future.