AI, predictive analytics top list of hot technologies for banks

Artificial intelligence, machine learning (a subset of AI) and predictive analytics are at the top of the list of hot technology investments for banks in 2022, according to market research firm Forrester, as they support strategic business decisions, help build applications that serve customers in a personalized way, and drive revenue growth.

This also includes hot technologies for banks 5G, Natural Language Processing (NLP), Microservices architectureand computer vision, according to the recent Forrester report Top Emerging Technologies in Banking In 2022.

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

Technologies are considered hot when banks have a planned investment in them over 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 creates more accurate predictions and analysis as it ingests data – continues to be of great interest as banks place a strong focus on cost while trying to improve customer experience and consumer spending to increase sales.

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

AI improves operational efficiency

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

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

The reason for the high interest is the insights these technologies can generate, enabling banks to make more informed business decisions and serve customers more individually, said Jost Hopperman and Martha Bennett, senior analysts at Forrester.

5G, NLP and microservices architecture are also technologies that banks are beginning to invest in, although they are of lesser interest than AI and analytics, the report shows.

5G supports customer service

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

Additionally, the market research organization said that natural language processing (NLP) and its subset, natural language understanding (NLU), are of moderate interest due to challenges including understanding local languages, dialects and accents.

According to Forrester, only 23% of respondents using AI in financial services use NLP and only 19% use NLU.

Meanwhile, computer vision, which Forrester says can be viewed as a specific application of machine learning, has seen increased interest as most banks use it for a comprehensive understanding of digital images or video for use cases ranging from identity verification to rich support of augmented reality projects.

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

Most chief technology officers believe microservices are critical when it comes to building new applications on top of a bank’s core legacy systems, Forrester noted. However, interest in microservices remains relatively low compared to AI and analytics. That’s because small and medium-sized banks often struggle to operate successfully in the devops environments typically used to build microservices, according to Forrester.

RPA, Blockchain are on the radar for banks

The report ranks any technology as “on the radar” if banks don’t plan to deploy it in the next 12 months but may consider it for pilot projects.

These technologies include deep learningAI powered Robotic process automation, expanded realityData Mesh (a distributed architecture for data management), Blockchain or distributed ledger technology, low code platforms, Progressive Web Apps, service network and Event-driven architectures.

Most of these technologies face different challenges such as regulatory compliance, quality checks, lack of trained staff, technological 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 “hot” in the report, the research firm said.

In addition, the report categories include technologies such as advanced gamification, confidential calculation, edge computing, quantum computingand internet of things than “hype” technologies.

As the name suggests, according to Forrester, these technologies are not mature enough for banking due to regulatory and security challenges, limited budgets, and a lack of clearly defined use cases.

Gartner highlights AI trend in banking

A report by Gartner, which identifies trends in the banking and finance sector in 2022, also highlights AI as a top trend in banking and forecasts that IT spending by banking and investment services firms will increase by 6.1% in 2022 to 623 billion US dollars will increase worldwide.

Generative artificial intelligence (AI), autonomous systems and privacy-enhanced computing (PEC) are three technology trends set to gain traction in banking and securities services in 2022, the market research firm said, adding that these trends will continue to grow in the next two years Gain momentum over three years to contribute to the growth and transformation of financial services companies.

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

“Generative AI enables bank CIOs to offer technology solutions to the enterprise to drive revenue growth, while autonomous systems and privacy-enhancing computations are long-term solutions that offer new options for business transformation in the financial services space,” said Moutusi Sau, vice president and Analyst at Gartner, in the report.

Market research firms define autonomous systems as self-managing physical or software systems that learn from their environment 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 needs and situations, optimize performance, and thwart attacks without human intervention, according to the market research firm.

Currently, autonomous systems in the banking context are mostly software-based, examples of which are humanoid robots in intelligent branches.