Smarter banking is on the way with GenAI

Cost savings for banks from AI applications could be $447 bn by 2023
Smarter banking is on the way with GenAI
GenAI in banking

Banks find themselves facing long-term trends that destabilize markets such as climate change, pandemics, cybersecurity threats, and even wars.

These change criteria are causing banks to evolve business models to meet new societal expectations and engage customers in digital environments.

Generative AI is still in its early stages and therefore continues to present the risk of critical errors and biased outputs.  Financial services firms are looking to source both internal and external data from market data providers, news feeds, risk data and historical data.  GenAI is unusable without this.

Yet, global Banks worldwide are exploring the potential of generative intelligence (GenAI) and machine learning (ML).

Read: Apple, Meta, throw their hats in the GenAI race

GenAI and banking revenues and savings

Some estimates show that aggregate potential cost savings for banks from AI applications would be $447 billion by the end of 2023.

Banks and the wider financial services sector can benefit from savings arising from GenAI, helping generate $200 bn to $340 bn annually, according to a report published by McKinsey last June.

According to estimates, the market for AI in fintech will reach $31.71 billion in 2027, growing at a rate of 28.6%. Already, AI is being applied in a number of different use cases. According to Cambridge Centre for Alternative Finance, 90% of fintech companies already use AI.

About 75 percent of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and R&D.

GenAI and banking: areas of assistance

Banks have already begun to grasp the potential of GenAI in their front lines and in their software activities, the report said, using solutions such as ChatGPT.

GenAI can also assist with risk management, fraud detection, and user engagement.

Banks using face and/or voice recognition biometrics to authenticate client transactions can, for example, couple their online banking app with AI to validate KYC transactions in real time.

Gartner identified generative AI as a top technology trend in 2022 for the banking and investment industry, contributing to data privacy, fraud detection, and risk management.

GenAI and Banking: Fraud Detection

Detecting anomalous and fraudulent transactions is one of the applications of generative AI in the banking industry.

Experts believe that using a Generative Adversarial Network (GAN) enhanced training set to detect such transactions produced successful outcomes because it develops sensitivity to identify underrepresented transactions.

GenAI banking

GenAI and Banking: Data Privacy

Financial data, especially relating to credit cards, is one of the most sensitive and personally identifiable data types. Financial institutions can leverage synthetic data, or data that is generated artificially based on real data, to overcome privacy issues. This is a major challenge that the banking industry is facing.

Synthetic customer data is very convenient for training ML models to help banks decide on loan decisions. They can approve a credit or mortgage loan and decide what that line of credit can be. GenAI also provides plausible reasons for such decisions helping explain denials or acceptance of such financial requests.

GenAI and Banking: Risk Management

In addition to software-specific systems that minimize risk, GenAI is a potential application for minimizing losses resulting from a lack of adequate risk management.

GANs allow calculating for value-at-risk estimations that show the potential amount of loss in a particular period of time. They also generate economic scenarios for predicting the future of financial markets.

GenAI and Banking: Other GenAI banking benefits  

Gen AI could assist banking employees in writing emails, creating business presentations and other project-related tasks. It can potentially help reduce costs associated with back-office operations like with call center staff. Customer-facing chatbots can for example assess and implement user requests.

GenAI evolving

Large language model ChatGPT was released in November 2022 and was followed some four months later by OpenAI with GPT-4. The latest version was equipped with much-improved capabilities.

By May 2023, Anthropic’s genAI, Claude, was able to process about 75,000 words per minute. Google announced several new features powered by GenAI including Search Generative Experience. It also released a new LLM called PaLM 2 that will power its Bard chatbot.

The financial system still has to cope with GenAI hallucinations, fibs that appear very credible. Financial institutions also need to transition to the modern era by adjusting their technology infrastructure. This infrastructure is powered by COBOL, a 1959 coding language that has largely remained idle for years. It is not a match to today’s rapid technological innovations. GenAI could be a replacement in the works.

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