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AI cho ngành ngân hàng

These days everybody is talking about the AI opportunity. But there is more focus on the what and less on the how. Let’s take a look at how AI is changing banking.

According to Mckinsey, banking could see the biggest impact as a percentage of their revenues (between 2.8% and 4.7%) from generative AI with the additional value calculated between $200 bn and $340 bn annually.

One of the main reasons for the high impact lies with the fact that banking has always been a data-intensive business. Across financial services and in every step of (any) consumer journey, from lending to payments, data is constantly generated and found at the heart of any interaction.

A large part of the recent innovation in FS is due to data. Not any kind of data, but the sort of open, rich, actionable data that can produce insights and decision making.

Real-time analytics, algorithmic trading, customer segmentation, predictive analytics or fraud detection are some of the tools of FinTechs to attack incumbents in their own turf.

On the other hand, leading banks have been riding the AI wave:

—     Goldman Sachs is using ChatGPT-style AI in house to assist developers with writing code

—     American Express started using machine learning in 2010 to help prevent fraud and now wants to use generative AI to enhance predictive analytics, aiming at understanding how customers will perform over time

—     Wells Fargo has deployed several AI-powered tools including virtual assistant Fargo (which uses Dialogflow, Google’s conversational AI) and its Customer Engagement Engine. Now it wants to become a digital leader (from a digital adopter) and implement AI at scale

—     JPMorgan Chase has been using large language models to detect fraud, by examining patterns in emails for signs of compromise

—     Ally Bank has piloted an AI-powered program that transcribes and summarizes customer service calls, a job previously done manually by contact center representatives.

During the disruption that ensued in the past 10 to 15 years banking was already in the middle of transforming from a human-based, relationship-first industry to a more automated and technology-driven business. What the abrupt introduction of gen AI does, is to exponentially increase the speed with which this transition is happening and to facilitate a mass-customization or hyper-personalization outcome. It’s only the combination of AI and data that can force two inherently opposite terms (mass and customization) to join forces under one umbrella.

If I were to summarize how AI will impact banking going forward, the answer would include 3 key principles / domains:

1) efficiency (driven my less manual work and increased automation, including decision making)

2) forecasting in the sense of better predicting customer patterns and managing the outcome and

3) helping banks deliver highly personalized experiences on a massive scale.

Opinions: my own, Graphic source: BCG