AI in Finance: The end or beginning of an era?

It's impossible to debate the role of AI in financial services without mentioning that COVID-19 shattered 2020, with the repercussions set to continue for years. Financial institutions' previously established digital transformation strategies for 2020 swiftly unraveled, exposing inefficiencies in reacting and responding quickly when the pandemic hit the world at breakneck speed. In actuality, most people are exposed to AI from morning to night. At a high level, AI can be defined as any approach that allows machines to solve tasks in the same way that people do.

It could be based on Machine Learning, which uses algorithms to allow computers to learn from examples without having to be explicitly programmed to make decisions; Natural Language Processing, which is concerned with generating meaning and intent from text in a readable, natural form; or Computer Vision, which is concerned with extracting meaning and intent from visual elements such as images and videos.

Why should banks prioritize AI?

Banks have continuously adapted to the latest technological breakthroughs to reinvent how customers interact with them over several decades. In the 1960s, banks introduced ATMs, and in the 1970s, computerized, card-based payments. The expansion of mobile-based "banking on the move" in the 2010s followed the widespread adoption of 24/7 internet banking in the 2000s.

Few would argue that we have entered the AI-powered digital age, which has been aided by dropping data storage and processing costs, more access and connectivity for all, and rapid advancements in AI technologies. These technologies can lead to increased automation and, when used after risk mitigation, can often outperform human decision-making in terms of speed and accuracy. AI has the potential to generate $1 trillion in added value for banks every year, making it one of the most valuable sectors.

AI technologies can help increase revenues by personalizing services for customers (and employees); lower costs by increasing automation, reducing errors, and better resource utilization; and uncover new and previously unrealized opportunities by improving the ability to process and generate insights from vast troves of data.

Disruptive AI technology can help banks accomplish four essential goals: better profits, at-scale personalisation, differentiated omni-channel experiences, and faster innovation cycles. Banks that fail to integrate AI into their fundamental strategy and operations, or become "AI-first," risk being surpassed by competitors and abandoned by their clients.

What may the future AI bank 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 omni-channel (seamlessly spanning the physical and online contexts across multi-device platforms) propositions and experiences.

Internally, the AI-first bank will be optimized for operational efficiency through the automation of manual chores (a "zero-ops" mindset) and the replacement or augmentation of human choices in many areas of bank operations by powerful diagnostic engines. Traditional and cutting-edge AI technologies, such as machine learning and facial recognition, will be used to analyze massive and complex pools of consumer data in (near) real time, resulting in improved operational performance.

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.

What are the barriers to banks using 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 fintech's speed, agility, and flexibility. They must, on the other hand, 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 efforts, few banks have been successful in disseminating and expanding AI 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' fundamental technology systems have worked admirably, especially when it comes to handling traditional payments and lending operations. However, before deploying AI technologies at scale, banks must address several flaws in legacy systems. To begin with, these systems frequently lack the capacity and flexibility needed to support the variable computing requirements, data processing needs, and real time analysis that closed-loop AI applications necessitate. Core systems are also difficult to change, and their upkeep necessitates significant resources. Furthermore, many banks' data repositories are dispersed across many silos (different business and technology teams), and analytics efforts are limited to certain use cases. It's nearly hard to analyze relevant data and provide intelligent advice or offer at the proper time without a centralized data backbone. If data is the bank's essential raw material, it must be managed and made available securely in a way that allows millions of customers to analyze data from internal and external sources in (near) real time at the "point of decision" across the company. Finally, in order to scale diverse analytics and advanced-AI models, companies require a comprehensive set of tools and established processes for building, testing, deploying, and monitoring models in a repeatable and "industrial" manner.

Artificial intelligence's entire impact on financial services yet to be seen. Some futurists believe the world is rapidly approaching a "singularity," a point at which computer intelligence will surpass human intelligence.

Many difficulties will arise as AI becomes more prevalent in our personal and professional lives. These include the possibility of human error, a general skepticism of computers, and fears about job replacement. Ignoring these concerns would be a mistake. Nonetheless, civilization is rapidly moving toward an AI-driven environment. In this new world, it may be more fruitful to concentrate on how machines and humans may coexist most effectively. Policymakers must exercise caution, enabling new technologies to grow while monitoring and mitigating their negative repercussions. To increase trust and pleasure with AI applications, developers and designers should improve humans' ability to understand AI systems. Everyone will play a part.