Artificial Intelligence in Finance with Examples: An Ultimate Guide

Artificial Intelligence has been the talk of the town, topping the market trends and charts.  Even the organisations that practised traditional business processes before the onset of Artificial Intelligence have had to re-structure their methodologies. Artificial Intelligence in Finance has emerged as one of the main areas of function for the technology.

One industry was massively impacted by the onset of Artificial Intelligence is the finance sector. For example, According to Insider Intelligence, over 50% of JPMorgan’s consumer banking represents its net income, which led them to implement AI technology for its account holders. According to Nvidia, State of AI in Financial Services report, over 75% of companies use at least one of the core computing applications, including High-Performance Computing (HPC), Deep Learning and Machine Learning. AI has a progressive scope in Finance. In this blog, you can learn more about Artificial Intelligence in Finance, its benefits & how will it affect the Finance industry.

Table of Contents

1) Understanding the applications of AI in Finance

   a) Consumer Finance

   b) Personal Finance

   c) Corporate Finance 

   d) Fraud Detection and Prevention

   e) Trading

   f) Credit Decisions 

   g) Robo Advisory

2) Examples of companies using AI in Finance

   a) JPMorgan Chase

   b) Darktrace

   c) Capital One

   d) Celo

3) Conclusion

Understanding the applications of AI in Finance

The revolution of AI has influenced even the financial industry, where innovations in Bayesian statistics were underway in the late 1900s. Bayesian statistics is a field of study that deals with understanding uncertainties amidst gigantic amounts of noisy data. This was the foundation of Machine Learning techniques because Bayesian statistics use Bayesian Probability to curate evidence for increased chances of a prediction.

Bayesian statistics has been utilised in stock markets and generating predictions for investors. Today, finance organisations use AI-supported robot advisers to provide fully automated, algorithm-based financial services with negligible human assistance.




Now let us take you through some notable applications of AI In Finance:

Consumer Finance

One of the most significant capabilities of AI is to intercept and block cyberattacks and fraud. These are very significant business use cases for Artificial Intelligence in Finance. If consumers look anywhere for reassurance, it is in the form of banks and other financial services that offer accounts with high levels of security. According to Juniper Research, 16 million GBP in online payment fraud losses were recorded in 2021 owing to the pandemic, data breaches and fraudsters exploiting machine learning.  

Humans would generally ignore AI’s capacity to analyse and separate pattern inconsistencies. For example, JPMorgan Chase is a bank that designed a proprietary fraud detection algorithm every time a credit card transaction occurs. These transaction details are then sent to Chase’s central computers in their data centres, then an assessment of whether the transaction was fraudulent. JPMorgan is globally reputed for its AI-supported infrastructure built for reliability and security.   

Although the advent of Artificial Intelligence would help transform a consumer’s financial condition, its limitations should be equally considered and respected. Owing to the reason for non-linear consumer protection problems, it may so happen that solutions can also give rise to new and unexpected problems.

Personal Finance

Financial independence is a hunger for every consumer out there, and offering the capacity to maintain a healthy financial condition for a consumer boosts the momentum for adopting AI. It has turned into a necessity for financial organisations to provide 24/7 guidance via chatbots supported by Natural Language Processing (NLP). Financial technology, or ‘Fintech’ as its catch-all term is coined, can be utilised as a tool to impart financial education with an in-depth view of personal money flow.

It must be noted that since Artificial Intelligence cannot replace human judgement completely in the domain of personal finance, it is equally crucial to consider the responsible use of it to maintain ethical practices. This is where financial professionals come into the picture to maintain protocols of caution and a guiding eye over the implementation of advanced Artificial Intelligence in Finance.

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Corporate Finance 

This area of Finance deals with fund sources and the capital infrastructure of corporations. All company decisions with a financial impact come under the umbrella of corporate Finance, which can be considered an alliance between the organisation and the capital market. An example of an area where organisations look to increase their value is loan underwriting, where information is obtained about the credit history and income of the loanee. Artificial Intelligence technology essentially identifies and assesses the loanee or loan seeker’s information in real-time and then recognises any inconsistencies in the loan seeker’s financial health.

Fraud detection and prevention 

AI-supported fraud detection systems analyse people’s spending behaviour and alert them if any transaction deviates largely from their traditional purchasing patterns. Fraud generally refers to any activity involving credit card, money laundering, asset misappropriation, intellectual property theft, trade secrets, etc. These thefts have increased due to the enormous rise in online transactions and the integration of third-party entities. This is where the financial industry is constantly pushing for improved machine learning techniques that can process and analyse large volumes of data. For example, Deep Neural Networks (DNN) outshine other techniques in their ability to process unstructured data and recognise patterns without requiring feature engineering, a method used to extract information from raw data.

Credit Decisions 

AI models are designed to gauge the creditworthiness of a loan seeker applicant so that lenders can make better decisions. Although human lenders are involved in the process, the more accurately the models score and segment populations, the fewer the applications go to manual reviewers. This results in money saved and improved customer experiences.

 
 

Trading

The saying ‘time is money’ is probably the most relevant concerning trading. This is because faster data analysis translates to faster pattern identification, resulting in better-informed decisions and trades. Markets react as soon as any pattern is identified, and the time for action passes, letting the opportunity out of hand.

This is why considerable efforts are invested in algorithmic trading, where complex systems take split seconds to make decisions and independently execute trades based on the identified pattern. It is to be noted that human traders also involve emotions in trade decisions, and this is where algorithms take the lead by making decisions purely based on history and logic.

Robo-advisory

Robo-advisors are wealth managers supported by Artificial Intelligence, where the algorithms curate portfolio recommendations based on the individual goals of the investors, risk preferences and disposable income. The investor is expected to make a monthly deposit, and the remaining work is managed for them, such as selecting and purchasing assets and rebalancing the portfolio. These steps ensure the best possible path for the customer to their goals.

Such online systems are very simple, and customers do not need any pre-requisite financial knowledge. These systems are even budget friendly as compared to human asset managerial services.

Examples of companies using AI in Finance

Let us take you through some real-world examples of organisations integrating Artificial Intelligence into their business processes and information architectures:

JPMorgan Chase

By market capitalisation, JPMorgan, the largest financial corporation in the United States of America, implements its proprietary algorithm for fraud detection patterns. It also has its data centres which receive credit card transaction details, followed by an assessment of the transactions’ fraudulent natures.

Darktrace

A leader in cybersecurity worldwide and focused on offering world-class technology protecting customers globally from modern threats, which include SaaS (Software as a Service) and ransomware attacks. Darktrace, at its essence, implements self-learning AI to enable machines to know the business, independently defending it as a result.

Capital One

A financial organisation specialising in banking, credit cards and auto loans, Capital One is reputed as a technology-centric bank. This organisation launched America’s SMS text-based assistant named ‘Eno’, which is designed for natural language I/O (input/output). This application generates insights and predicts customer needs, such as alerting customers about potential fraud.

Celo

Celo is a Blockchain Technology designed for mobiles and optimised for payments between peers using only a mobile number. This technology is compatible with Ethereum and can reach users worldwide and convert cryptocurrency into usable money. Celo’s ultimate goal is to make the financial activity accessible to anyone worldwide thanks to its peer-to-peer mobile base. This technology’s decentralised structure allows anyone to contribute as a crowdfunding platform, even for social causes.

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Conclusion

You may have realised, at this point of the blog, how unsurprising it is to foresee the involvement of Artificial Intelligence in Finance. Considering how the pandemic bought a paradigm shift in human interaction, experts predict AI will save the banking industry by the next decade. It turns out to be more of a necessity if banking organisations have to remain relevant. More importantly, the largest consumer group for banks consist of Millennials and the Gen Z population, which translates to meeting higher digital standards, especially because younger consumers value digitisation the most. Over 75% of Millennials state they will avoid a trip to the bank if they have another option.

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