Artificial Intelligence (AI) in banking is a leading force to reckon with. The technology—which enables machines to simulate and augment human intelligence—has manifested the potential to drive tangible, real-world business outcomes for the industry.
An OpenText survey revealed that most banks (80%) are well-aware of the benefits of AI and believe that technology is the business advantage of the future. In fact, most of them have started with AI initiatives. A USB Evidence Lab report found that almost 75% of banks with over $100 billion in assets say they are currently implementing AI strategies, compared to 46% of banks with less than $100 billion in assets. In another report by Autonomous Next Research, the aggregate potential cost savings for banks, attributable to their AI initiatives, is estimated to reach $447 billion+ in 2023.
These numbers are proof that AI has finally descended from the realm of fiction, bearing a groundbreaking potential for the banking industry.
Artificial Intelligence: Galvanizing Growth for Banking
By letting machines to learn, adapt, and improve, AI isn’t only reshaping the operational structures across the board, but also relationships—with employees, customers, and ecosystem partners—that ensure business success. Below are some of the areas where technology is helping banks differentiate and beat the competition.
- Customer Experience
AI is making customer service more efficient than ever. Today’s chatbots are evolving to interact with humans using contextual learning, which means that they are outgrowing their potential to think and respond like humans and providing relevant solutions based on real-time data. Juniper, in a report, has predicted that chatbots will be responsible for cost savings of up to $8 billion by the end of 2022, making it clear that the era of chatbots with human intelligence and awareness is here. In addition to this, AI systems have been helping banks analyze customers based on past interactions and customize financial products and services to meet their needs.
- Process Automation
Banks have begun capitalizing on the power of AI to compete effectively in an increasingly saturated market. The technology has helped banks solve multiple internal challenges, which includes lower efficiency and increased labor costs. AI has radically streamlined a variety of back-office processes that once reduced productivity. By letting machines take over most of the manual and repetitive tasks, banks have been able to create a work environment where employees are focused on strategic and rewarding tasks, which exponentially increase performance, efficiency, and resilience.
- Event Forecasting and Predictive Maintenance
AI has been helping banks see and shape the future. By deriving actionable insights from data, technology has made it possible for banks to predict events and anticipate risks. As a result, the combative measures to control fraud and money laundering have changed from prescriptive to predictive, saving millions. Using key recommendation engines, banks have also been able to up-sell and cross-sell their products and services to various customers.
- Effective Decision-Making
Cognitive systems, enabled by AI, use augmented intelligence to uncover insights and provide optimal solutions. These systems use sophisticated algorithms and ensemble techniques to extract value from data and identify patterns and anomalies that help banks make consistent decisions based on existing as well as evolving business rules.
- Improved Recall and Precision
The application of AI in banking has been effective in handling manual processes and reducing the possibility of errors thereby. The technology has introduced bots to enable automation of voluminous tasks such as data processing, which not only prevents errors but also saves costs by over 30% - 70%.
AI in Banking is Emerging But Not without Challenges
- AI is still operating within the data conundrum. Most banks have huge but unspecified data, which results in faulty insights and derails the entire decision-making process. Imagine, for example, the severity of risks that could arise from KYC compliance processes if data isn’t verified, or the consequences of building fraud prevention strategies without the right data.
- There is a sense of hesitation around AI as it is believed that technology will wipe out jobs for humans. It’s no secret that humans are far more fallible than computers, and this insecurity forms the basis for employees’ resistance to AI. Inspiring trust in AI is perhaps the biggest roadblock to its adoption in banking.
- Scaling AI is where most banking firms find themselves at a loss. Confusion over how to define workflows, to what extent automation must be present, and where human intervention is required, still persists. Most enterprises have started implementing AI but aren’t ready for AI-first thinking.
- Banks have a bigger challenge in addressing the cultural shift in order to best leverage AI. Every firm, even today, has a pool of legacy people, who aren’t receptive to any technology shift, let alone AI. These people show resistance to change, which means that, even if banks manage to adopt digital transformation, adoption without people actually using the new technology is difficult to achieve.
- There aren’t enough AI experts who can offer a touch of reality to the rapidly progressing realm. In a recent Ernst and Young poll, 56% of respondents agreed that lack of talent and qualified workers is the single biggest bottleneck, impeding AI’s success.
The Future is Intelligent Banking
AI has brought a revolution in the banking sector. The technology is helping firms cross out traditional processes to introduce new, innovative methods that improve efficiency and create better experiences for customers and employees alike. However, the challenges of implementation are overwhelming. To make the best use of AI, banking institutions are required to rewire their DNAs. They need to find a middle ground where AI can work with employees in tandem and ensure that the technology is used responsibly. And, all of this can be achieved if banks find a way to bring people, processes, and data together and be persistent in the face of change.