Success is in the AI of the beholder - Global Banking | Finance (2024)

It seems like only yesterday that Artificial Intelligence (AI) was the stuff of science fiction; a twinkle in the eye of the few computer scientists privy to its potential; a concept, rather than grounded in reality.

To suggest today––as many have––that AI is a buzzword, is to vastly underestimate how it will radically alter almost every aspect of our daily lives in the coming decades.

The great AI space race is well underway. Governments and business alike are pouring billions into the field. The AI market could be worth $46 billion within the next three years, according to the International Data Corporation, based on an anticipated annual growth rate of about 54 per cent.

Every industry bar none will evolve with Darwinian effect. Those which invest now will survive. Those which fail will go the way of the dodo. Any repetitive task, especially those involving large datasets, will in someway harness artificial intelligence in the near future.

This isn’t shiny new toy syndrome––it’s evolve or die.

Banks have employed AI, at least in rudimentary forms, for decades. Computer automation has been used by the financial industries for back office operations since the sixties. But remarkably, many haven’t invested early enough to stay ahead of the curve in other areas.

I say ‘remarkably’ because few industries outside of tech generate anything close to the vast quantities of data that banking does. The rise and rise of mobile banking has opened the door to exponential growth in the data financial institutions generate, process and hold. More data points leads to better results. Better results lead to more customers. More customers create more data, and so on.

The information held about millions of customers is complimented only by banks having the structure and capital to properly exploit it. Using AI, data covering anything––location, spending habits or balance, to name a few––can each be harnessed to produce better results at all levels of a business, from high street branch to marketing department, engendering a better customer experience overall.

Open Banking, and the second Payment Services Directive, implemented in February this year, demonstrated empirically the commercial value of data held by banks. Lenders were slow to exploit this potential value post-crisis, while fintechs steamed ahead, muscling in on banks’ turf.

Some banks are getting the hang of it. Take the app, Chip. Born in the Barclays incubator, Chip uses AI to analyse your spending habits and calculate what you can afford to stash away. Every few days, it then automatically transfers small, often-unnoticeable, amounts of money from your current account to your Chip account. You save money while barely noticing it. It’s a simple application of powerful technology, but it works.

A good start, but beyond personal finance tools and chatbots, high street lenders are only just catching up with the pace of AI innovation. The way customers interact with banks is evolving, and CMOs would be wise to stay apace, or risk falling behind.

By deploying excellent AI products, CMOs will improve their customer interactions, and the quality of their offering. Attracting new customers is a never-ending battle in the subscription economy, especially so in an industry with famously loyal customers.

A recent Feefo survey found that while customers generally regard themselves as loyal, more than six out of 10 under-35s see themselves switching providers more often in future. This generation wants to engage with banks in more meaningful ways––which presents opportunities for savvy marketing teams.

Artificial intelligence is perhaps the best way for CMOs to provide the meaningful experiences customers crave. Using the masses of data they produce, banks and financial services providers can get to know their customers on a far more intimate level, and communicate as necessary.

Banks’ marketing departments are some of the biggest investors in advertising, spending $17.1 billion per year at last count. Our research has found that a media plan requires roughly 5,000 decisions. It’s easy to see why AI is an attractive option for taking over that heavy lifting.

Systems that use intelligent machine learning are able to absorb masses of data and identify which factors contributed the most to a certain metric at any given time, allowing marketers to properly analyse the ‘true’ effects of their advertisem*nts. A more granular, detailed understanding of their marketing effectiveness allows marketers to employ different media mixes for different purposes, such one for retaining current customers and another for obtaining new ones, and to keep an accurate pulse on their effectiveness.

On average, at Blackwood Seven our customers experience a measurable 20 per cent sales uplift from their media investments when they use AI, worth millions in revenue growth. There is low hanging fruit, ripe for the picking. There’s nothing artificial about that.

Share on FacebookShare on TwitterShare on Linkedin

Success is in the AI of the beholder - Global Banking | Finance (2024)

FAQs

How does AI help with banking? ›

AI for corporate banking automates tasks, boosts customer services through chatbots, detects fraud, optimizes investment, and predicts market trends. This increases productivity, lowers costs, and provides more individualized services. Q. How AI helps in banking risk management?

How does generative AI affect banking? ›

In banking, this can mean using generative AI to streamline customer support, automate report generation, perform sentiment analysis of unstructured text data, and even generate personalized financial advice based on customer interactions and preferences.

What is the transformative effect of AI on the banking industry? ›

AI not only enhances the customer experience but also drives strategic decision-making. Predictive analysis systems use large datasets to forecast trends, risks, and opportunities, providing a significant competitive advantage in the financial market.

What is the role of AI in investment banking? ›

AI and machine learning help banks find scams, reduce risks, find holes in their systems, and make online finance more secure. By leveraging AI, banks can identify real-time suspicious activities, like money laundering or fraudulent transactions.

Which bank is using AI? ›

Capital One is another example of a bank embracing the use of AI to better serve its customers.

What are the disadvantages of AI in banking? ›

4 Disadvantages of AI in the Financial Sector
  • Expensive. Artificial intelligence requires a lot of money for production and maintenance because it is a highly complex machine. ...
  • Bad Calls. ...
  • Unemployment. ...
  • Clients remain suspicious of AI.

What problems can generative AI solve? ›

Generative AI solves diverse business problems, such as adapting to consumer preferences, streamlining content creation, and enhancing data-driven decision-making. It's pivotal in optimizing design processes, advancing healthcare innovations, and improving financial forecasting.

How is JP Morgan using generative AI? ›

Overall, J.P. Morgan Research estimates generative AI could increase global GDP by $7–10 trillion, or by as much as 10%. The technology could result in a massive workforce productivity boom over the next one to three years, which could affect the shape of the economic cycle.

What is the downside of generative AI? ›

Known Limitations Of Generative AI

Large language models (LLMs) are prone to "hallucinations" - generating fictitious information, presented as factual or accurate. This can include citations, publications, biographical information, and other information commonly used in research and academic papers.

What are the biggest challenges in implementing artificial intelligence in banking? ›

The use of AI in banking has raised several ethical and legal concerns, including privacy, security, lack of transparency and algorithmic bias. In terms of privacy, AI systems pose challenges concerning how they may process or store personal data without the proper permissions.

What are the ethics of AI in banking? ›

By using AI, many ethical issues were identified which includes the wrong credit scoring, sharing of wrong information, miss selling, and unauthorised trading in the banking. The AI applications and methods significantly changed and enhanced the entire financial and non-financial ...

What are the negative effects of AI in finance? ›

Biases And Discrimination

In the financial services sector, bias can come in various forms, such as racial or gender-based discrimination, socioeconomic bias and other unintended preferences, which could impact credit and investment decisions, hiring practices and even customer service.

How can AI help in banking? ›

AI is used in banking to enhance efficiency, security, and customer experiences. It automates routine tasks like data entry and fraud detection, reducing operational costs. AI-driven chatbots provide 24/7 customer support.

Will AI take over investment banking? ›

AI will change how businesses operate and can transform investment banking, but it won't replace bankers soon. AI may simplify tasks and improve decision-making, but investment banking relies on human perception and connections. AI may eliminate some jobs but generate others. Thus, a complete replacement is impossible.

How are banks using Generative AI? ›

Financial institutions are using the tech to generate credit risk reports and extract customer insights from credit memos. Gen AI can generate code to source and analyze credit data to gain a view into customers' risk profiles and generate default and loss probability estimates through models.

What are the benefits of AI chatbots in banking? ›

What are the benefits of AI chatbots in banking?
  • Chatbot for 24/7 availability. ...
  • Cost savings with chatbot implementation. ...
  • Instant response via AI chatbot. ...
  • Handling high volume requests with a chatbot. ...
  • Linguistic flexibility of chatbots. ...
  • Improved customer experience through chatbots.
Sep 2, 2023

How can central banks use AI? ›

Given the growing imperative for operational efficiency, coupled with the escalating complexity of data, central banks are increasingly embracing AI-driven solutions to streamline and accelerate data analysis, bolster risk management, refine policy formulation and forecasting accuracy, and enhance the detection of ...

Which is the most used AI technology in banking and finance? ›

Chatbots & Virtual Assistants

Chatbots and virtual assistants powered by AI have become a staple in modern banking. These applications use natural language processing (NLP) and machine learning algorithms to understand and respond to customer queries in real-time.

How is JP Morgan using AI? ›

J.P. Morgan is also using AI for payment validation screening and to automatically show insights to clients, such as cashflow analysis, when they need it.

Top Articles
Latest Posts
Article information

Author: Dr. Pierre Goyette

Last Updated:

Views: 6570

Rating: 5 / 5 (70 voted)

Reviews: 93% of readers found this page helpful

Author information

Name: Dr. Pierre Goyette

Birthday: 1998-01-29

Address: Apt. 611 3357 Yong Plain, West Audra, IL 70053

Phone: +5819954278378

Job: Construction Director

Hobby: Embroidery, Creative writing, Shopping, Driving, Stand-up comedy, Coffee roasting, Scrapbooking

Introduction: My name is Dr. Pierre Goyette, I am a enchanting, powerful, jolly, rich, graceful, colorful, zany person who loves writing and wants to share my knowledge and understanding with you.