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Home > Financial Services >  Retail Banking >  Artificial Intelligence (AI) in Banking – Thematic Research

Artificial Intelligence (AI) in Banking – Thematic Research

AI is disrupting all industries, nowhere more so than in financial services. Banks are being challenged by cloud-native fintechs, who can use their expertise and agility to offer a completely revolutionized customer experience. With data now being viewed as the new oil, incumbent banks must do everything they can to rationalise and use the vast swathes of customer data that they hold. With the correct tools, banks can leverage this data to improve their non-performing loan (NPL) ratios, increase their loan coverage, and guard against ever-evolving cyberattacks. To this, banks will need full visibility of the competitive landscape in the industry and the leading technology vendors that they can partner with to make this mission a reality. Failure to quickly identify and combat the threat posed by AI, through digital innovation, will lead to the disruption of incumbent firms out of the market in the long term.

Scope

Forecast data (2019-2024) of the estimated evolution in banking spend on AI services globally.

A breakdown of the AI value chain and its intersection with the banking stack, including investment recommendations for each technology in each section of the banking value chain.

An explanation of how AI can help to solve each of the challenges faced by the banking industry including cybersecurity risks, the threat posed by digitally-native fintechs, and falling profitability driven by macroeconomic conditions such as perennially low interest rates and COVID-19.

Analysis of the the key AI technology vendors in banking, including their specialisms, their location in the AI value chain, and details of some existing partnerships.

Summaries of the incumbent and disruptive banks adopting AI and transforming their businesses. These detail the specific AI technologies they use, the partnerships they have as well asplacing them in the context of the AI value chain.

Key best practice case studies of AI adoption in banking, supported by executive interviews with the companies involved.

GlobalData’s thematic sector scorecard for retail banking ranks the leadership of 37 banking companies in AI and other themes disrupting their industry. This is informed by GlobalData’s comprehensive tracking of AI related deals, job openings, patents ownership, company news, financial and marketing statements.

Key Highlights

GlobalData recommends that banks invest in both machine learning (ML) and data science across the entire banking value chain. Both technologies have large and diverse potential to improve the banking stack. This includes using data analytics to conduct more comprehensive risk assessments for loans, whilst reducing the time taken.

AI is the next step in the cybersecurity journey and is becoming necessary to protect against ever-evolving cybercriminals. Banks hold massive swathes of customer data that is often segmented or unstructured and it is imperative that banks take control of this data so that it can be properly analyzed and manipulated. ML and data science tools are being used to combat cyber threats.

In 2018, Tencent’s WeBank had an average IT operating cost per user of $0.50 per year, compared to around six to 30 times that figure at most incumbent banks. These cost savings were largely driven by the use of cloud and AI to optimize and automate all sales and service pathways.

According to GlobalData’s Investment Intensity Map, which ranks industries based on their exposure to particular themes, banking scores higher than any other industry in terms of AI adoption.

Reasons to buy

Identify leading artificial intelligence vendors in banking and shortlist potential partners based on their areas of expertise and historic partnerships.

Benchmark your company against 37 other banks in terms of how prepared each business is for AI disruption. Use this as a roadmap for where to target your AI investment and to focus on areas of the market that are underserved.

Develop marketing messages and value propositions for your AI services that will resonate with prospective clients in the banking industry, by uncovering the business challenges they face in areas such as falling profitability, cybersecurity, and COVID-19. Pick the right areas to invest in AI-based on our value chain recommendations, paired with case study insight.

As a technology vendor, identify the areas where banks are most in need of your services and uncover the areas that are lacking specific AI vendors that might prove profitable areas for expansion.

Support investment cases for AI in banking, both as a vendor and as a bank, by accessing our proprietary market sizing and growth forecast data, specific to AI in financial services.

Companies mentioned

Google, Amazon, Microsoft, Alibaba, Baidu, Apple, Tencent, Facebook, IBM, Temenos, FiVerity, Simudyne, Personetics, Fiserv, Kasisto, Tencent, Ant Group, Goldman Sachs, BBVA, OakNorth, DBS Bank, Bank of America, Santander, Samsung

Table of Contents

| Contents

Executive summary

AI value chain

Machine learning

Data science

Conversational platforms

Computer vision

AI chips

Smart robots

Context-aware computing

Banking challenges

The impact of AI on banking

Case studies

Market size and growth forecasts

Mergers and acquisitions

AI timeline

Companies

Sector scorecard

Glossary

Further reading

| Our thematic research methodology

| About GlobalData

| Contact Us

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