Is AI the Future of Banking?


Artificial intelligence (AI) is becoming more and more integrated into our daily lives. Banks also must employ AI at scale to stay relevant.
A holistic transformation encompassing various layers of the organization is required for success.

Artificial intelligence (AI) technologies have progressed even farther since then. And their revolutionary impact is becoming more apparent across industries.AI-powered algorithms are tailoring digital content recommendations to individual tastes and inclinations. They developlalaa clothing lines for merchants, and even beginning to outperform expert doctors in identifying cancer indications.
McKinsey forecasts that AI technology might bring up to $1 trillion in value to the global banking industry per year. 

Many banks, on the other hand, have failed to scale AI technologies across the business after experimenting with a few use cases.
Lack of a defined AI strategy, an inflexible and investment-starved technology core, fragmented data assets, and obsolete operating methods that stymie collaboration between business and technology teams are among the reasons. Furthermore, various digital interaction trends have intensified as a result of the COVID-19 epidemic. And big-tech firms are eyeing financial services as the next adjacency. Incumbent banks must become “AI-first” organizations in order to compete successfully and survive. They use AI technology as the foundation for new value propositions and unique client experiences.

1. Why must banks become AI-first?

Banks have continuously adapted the latest technological breakthroughs to reinvent how customers interact with them over several decades.
In the 1960s, banks launched ATMs, and in the 1970s, they introduced computerized, card-based payments. The adoption of 24/7 online banking was widespread in the 2000s, followed by the widespread adoption of mobile-based “banking on the go” in the 2010s.

Few would argue that we have entered the AI-powered digital age.
These technologies can lead to increased automation. When used after risk mitigation, can often outperform human decision-making in terms of speed and accuracy.
AI has the potential to unleash $1 trillion in added value for banks each year. It can make one of the most valuable industries in the world.

AI technologies can boost revenues through increased personalization of services to customers (and employees); lower costs through efficiencies generated by higher automation, lower error rates, and better resource utilization; and uncover new and previously unrealized opportunities based on an improved ability to process and generate insights from vast troves of data across more than 25 use cases.

Disruptive AI technology can help banks accomplish four essential goals: better profits, at-scale personalisation, distinct omnichannel experiences, and quick innovation cycles. Banks that fail to integrate AI into their fundamental strategy and operations, or “AI-first,” risk being overrun by competitors and abandoned by their customers.

Banking on artificial intelligence | E&T Magazine

2. What might the AI-bank of the future look like?

To meet customers’ rising expectations and combat competitive threats in the AI-powered digital era, the AI-first bank will offer intelligent (that is, recommending actions, anticipating and automating key decisions or tasks), personalized (that is, relevant and timely, and based on a detailed understanding of customers’ past behavior and context), and truly omnichannel (seamlessly spanning the physical and online contexts across multi-device platforms) propositions and experiences.

Traditional and cutting-edge AI technologies, such as machine learning and facial recognition, will be used to analyze massive and complex pools of client data in real time.

The AI-first bank of the future will have the same speed and agility as today’s digital-native businesses. It will be quick to innovate, releasing new features in days or weeks rather than months.
It will work closely with partners to develop new value propositions that are seamlessly integrated across experiences, platforms, and data sets.

3. What obstacles prevent banks from deploying AI capabilities at scale?

At first appearance, incumbent banks must balance two sets of goals that appear to be at odds. On the one hand, banks must emulate the fintech’s speed, agility, and flexibility. They must continue to manage the scale, security standards, and regulatory obligations of a traditional financial services firm.

Despite billions of dollars spent annually on change-the-bank technology programs, few banks have been successful in disseminating and growing AI technologies across their organizations.
One of the most significant roadblocks to banks’ efforts is the lack of a clear AI strategy.
Many banks also face two additional challenges: a shaky core technology and data backbone, and an outdated operating model and talent strategy.

Banks’ core technology systems, which were designed for stability, have fared well, particularly in sustaining traditional payments and lending operations.
However, before banks can implement AI technologies at scale, they must address key flaws in legacy systems.

Core systems are also difficult to change, and their upkeep necessitates a large investment of time and money.
Traditional banking operating paradigms obstruct banks’ ability to meet the demand for constant innovation.

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