AI, predictive analytics top list of hot technologies for banks

Artificial intelligence, machine learning (a subset of AI), and predictive analytics top the list of hot, planned technology investments for banks in 2022, due to their ability to aid strategic business decision-making, help build applications that can serve customers in a personalized manner, and drive revenue growth, according to market research firm Forrester.

Hot technologies for banks also include 5G, natural language processing (NLP), microservices architecture, and computer vision, according to Forrester’s recent Top Emerging Technologies in Banking In 2022 report.

The report, based on survey responses from tech decision makers in banks and their technology vendors, categorizes 30 different technologies into three main categories: “hot,” “on-the-radar,” and “hype.”

Technologies are considered hot if banks have lined up a planned investment in them in the next 12 months, Forrester said, adding that these new investments are expected to shape the future of the banking industry and customer experience.

Among the hot technologies, artificial intelligence and machine learning — a subset of AI that that makes more accurate forecasts and analysis as it ingests data — continue to be of high interest as banks keep a strong focus on costs while trying to boost customer experience and revenue.

“Machine learning helps improve process automation across processes like loan origination and fraud detection and can help deliver a more personalized experience,” Forrester said in the report.

AI enhances operational efficiency

Nearly 37% of survey respondents who are already using artificial intelligence in financial services consider improved operational efficiency a benefit of using AI, the report shows. Almost 33% of respondents claim that machine learning can lead to improved customer experience.

Real-time and predictive analytics is another hot technology for banks, with nearly 89% of survey respondents confirming that they are either in the planning, implementation or operational phases of using these technologies, the Forrester report shows.

The reason for the high interest is due to the insights that these technologies can generate, giving banks the ability to make better-informed business decisions and serve customers in a more personalized fashion, said Jost Hopperman and Martha Bennett, principal analysts at Forrester.

5G, NLP, and microservices architecture are also technologies that banks are starting to invest in, though they of more moderate interest than AI and analytics, the report shows.

5G aids customer service

5G is expected to become a general-purpose technology for the financial services sector as most organizations start using it for low-latency communications, Forrester said. While 5G infrastructure is just starting to ramp up, almost 56% of respondents believe that customer service is a major use case for the technology, the report said.

Further, the market research organization said that natural language processing (NLP) and its subset, natural language understanding (NLU), is of moderate interest because of challenges including comprehension of local languages, dialects, and accents.

Only 23% of respondents who use AI in financial services use NLP and only 19% use NLU, according to Forrester.

Meanwhile, computer vision, which can be considered a specific use of machine learning, according to Forrester, has seen a rise in interest, with most banks using it for high-level understanding of digital images or videos for use cases ranging from identity verification to support of augmented reality projects.

Another area of interest is microservices, the market research firm said, adding that almost 35% and 33% of developers in financial services use microservices and containers, respectively.

Most chief technology officers believe that microservices are crucial in efforts to build new applications on top of a bank’s core, legacy systems, Forrester noted. Interest in microservices remains relatively low compared to AI and analytics, however. That’s because smaller and mid-size banks often struggle to successfully work in the devops environments usually used to build microservices, Forrester said.

RPA, blockchain are on the radar for banks

The report categories any technology to be “on-the-radar” if banks are not planning  deployments in the next 12 months, but may be considering them for pilot projects.

These technologies include deep learning, AI-powered robotic process automation, augmented reality, data mesh (a distributed architecture for data management), blockchain or distributed ledger technology, low-code platforms, progressive web apps, service mesh and event-driven architectures.

Most of these technologies face varying challenges such as regulatory compliance, quality checks, shortage of trained talent, technology know-how, failed projects and negative or no return on investment.

In fact, implementing some of these technologies would require banks to first successfully deploy technologies branded as “hot” in the report, the market research firm said.

In addition, the report categories technologies such as advanced gamification, confidential computing, edge computing, quantum computing, and IoT as “hype” technologies.

As the name suggests, these technologies, according to Forrester, are not mature enough for banking because of regulatory and security challenges, constraints in budgets and lack of well-defined use-cases.

Gartner also highlights AI trend in banking

A report from Gartner identifying trends in the banking and financial sector in 2022 also highlights AI as a top trend in banking, and forecasts that IT spending by banking and investment services firms will grow 6.1% in 2022 to $623 billion globally.

Generative artificial intelligence (AI), autonomic systems and privacy-enhancing computation (PEC) are three technology trends gaining traction in banking and investment services in 2022, the market research firm said, adding that these trends will continue to gain momentum over the next two to three years, contributing to growth and transformation of financial services organizations.

The market research firm defines generative AI as the use of artificial intelligence and machine learning to generate insights from data to take operational decisions. Banking use cases include  fraud detection, trading prediction, synthetic data generation and risk factor modelling.

“Generative AI enables bank CIOs to offer technology solutions to the business in pursuit of revenue growth, while autonomic systems and privacy-enhancing computation are long-term solutions that provide new options for business transformation in financial services,” said Moutusi Sau, vice president and analyst at Gartner, in the report.

The market research firms defines autonomic systems as self-managed physical or software systems that learn from their environments and dynamically modify their own algorithms in real-time to optimize their behavior in complex ecosystems.

These systems create an agile set of technology capabilities that support new requirements and situations, optimize performance and defend against attacks without human intervention, the market research firm said.

Currently, autonomic systems are mostly software-based in the banking context and examples include humanoid robots in smart branches.