AI in the financial services industry continues to be a topic of discussion in regards to under-utilised technology. The aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023, with the front and middle office accounting for $416 billion of that total, according to Business Insider Intelligence.
Those in the banking sector have the ability to use AI to transform the customer experience by enabling frictionless, 24/7 customer interactions. However, customers are not the only one to benefit.
AI is particularly useful to save costs in the front office through conversational banking, middle office in anti-fraud, and the back office for underwriting, but how? Here are 4 ways AI is being used in banking and financial services.
For those who enjoy a light read, below is a quick summary:
- Calculate borrowing power in credit decision making
- Manage risk through accurate forecasting and prediction
- Personalise your customers banking experience
- AI powered search engines for quantitative trading
- Artificial intelligence: Is it a priority?
Calculate Borrowing Power in Credit Decision Making
The days of paying with cash are dwindling, a study in 2019 found 77% of consumers preferred paying with a debit or credit card compared to only 12% who favored cash. But easier payment options isn’t the only reason the availability of credit is important to consumers. Customers with good credit aid in receiving favorable financial options, landing jobs, and renting an apartment or home.
The use of Automated Machine Learning (AML) is being used by financial institutions to help calculate borrowing power of consumers with little to no credit information or history. Platforms like these utilise thousands of data points and provide transparency that other methods cannot. This helps lenders better assess populations traditionally considered “at risk”.
AI is also used for accurate predictive modeling to enhance decision making around issues like fraudulent credit card transactions, digital wealth management, direct marketing, blockchain, lending and more. These machine learning platforms provide financial institutions with more transparency whilst reducing losses.
Manage Risk through Accurate Forecasting and Prediction
Risk can cripple a financial institute if not given the appropriate attention. Accuracy in forecast predictions is crucial to the speed and protection of businesses in finance. Using machine learning to reduce margins of error, pinpoint trends, and conserve manpower is no longer a nice-to-have, but a necessity.
Using combinations of cloud computing and natural language processing (NLP), financial institutions can provide answers to complex questions in layman’s terms. These systems can not only support accuracy, but aid in anti-money laundering detection to accelerate investigations.
Personalise Your Customers Banking Experience
Regardless of generational placement, consumers are becoming more and more tech savvy. A study by Accenture of over 30,000 banking customers found 54% want tools to help them monitor their budget and make real-time spending adjustments. Additionally, 41% are very willing” to use computer-generated banking advice. Chatbots and AI assistants create personalised financial advice and natural language processing that provides instant, self-help to customer service.
Conversational AI systems automate aspects of the customer experience with accuracy through reducing call center traffic volumes providing customers with additional conversational options, like self-service. AI-powered chatbots also provide users with calculated recommendations and assist with other financial decisions.
Virtual financial assistants are also helpful and more personal ways to provide customers with methods of convenient banking. These solutions integrate with Google Home, SMS, Facebook, Amazon Alexa, web and mobile, and this will continue to expand in 2021. These assistants provide simple knowledge, support requests to personal financial management, and conversational banking.
And finally, AI money-saving assistants that connect with users accounts to analyse spending. These smart applications can cancel subscriptions that are not being utilised, find alternative options for services like insurance, and support with bill negotiations. We can expect to see many more personalised banking methods in the years to come.
AI Powered Search Engines for Quantitative trading
The process of using large data sets to identify patterns and strategic trade is one of AI’s most useful use cases. These algorithms automate the trading process and save valuable time.
AI-powered search engines specifically for the finance industry serve clients like banks, investment firms, and Fortune 500 companies. These systems use natural language processing (NLP) to discover changes and trends in financial markets. For example, brokers and traders can use these systems to access SEC and global filings, earning call transcripts, and press releases for both public and private companies.
Another use case is AI-powered stock rankers that analyse large data sets like price patterns. These systems simplify information into a numerical rank order for stocks. The greater the score, the more likely this stock will outperform the market.
Artificial Intelligence: Is it a Priority?
In short, yes. With bank revenues exceeding incomes of entire nations, there is no reason not to budget artificial intelligence and machine learning as a priority. AI is projected to reduce banking operational costs by 22% by 2030. With the ability to afford innovation, the question in 2021 is “what’s next?”, as every financial institute has the opportunity to innovate not only within the industry, but across all industries globally.
In summary: Leveraging Automated Machine Learning (AML), Natural Language Processing (NLP), can conversational Artificial intelligence, can provide a plethora of use cases for credit decisioning, risk management, personalised banking, and quantitative trading.
We hope you found this blog useful! To learn more on Artificial Intelligence in banking and finance, download ‘The Digital Economy in 2021: Banking and Finance’ white paper.
Crystal Delta is a global software engineering practice specialising in banking, finance, and education. Contact us today for more information on how we can support your software and engineering
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