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Competitive Survival in Banking Hinges on Artificial Intelligence

The 'AI in Banking' Digital Banking Report surveyed banks and credit unions globally to determine the extent of development of artificial intelligence functionality in the banking industry. The findings indicate that industry-wide deployment is far behind other industries.

Subscribe TodayThe door is wide open for forward-thinking financial institutions to leverage the data, insights and advanced analytical tools at their disposal to improve back-office operations and contextual personalization. That said, research indicates that most banks and credit unions – and the industry as a whole – have not kept pace with consumer expectations around digital capabilities or digital engagement compared to what other industries are providing.

Nowhere is this more evident than with the banking industry’s deployment of artificial intelligence (AI). With an origin rooted in risk and fraud detection and cost reduction, AI is increasingly important for financial services firms to be competitive. The digital consumer is being trained by firms that are becoming masters of AI (Amazon, Google, Facebook and Apple) and expect the companies they use to know them, understand them and reward them through personalized communication.

According to the , , sponsored by and the , while organizations understand the importance of artificial intelligence, few are deploying AI solutions, with many organizations not even considering artificial intelligence in the next 18 months.

Additional takeaways from the report include:

  • Larger financial institutions are far ahead of smaller banks and credit unions in the deployment of AI solutions.
  • Security and risk applications are the most deployed solutions, followed by personalization and communication applications.
  • Despite the deployment of security applications, an enhanced customer experience was the primary AI business case driver mentioned for AI deployment.
  • There is no consensus of who should lead AI initiatives. Most are lead by cross-functional teams.
  • The primary challenge for firms wanting to implement AI solutions is the lack of expertise and talent.

AI Deployment

According to the Digital Banking Report, only 15% of the organizations are using AI to compete with peers and identify opportunities in their data that would otherwise be missed. Another 22% are expecting to use AI functionality in the next 18 months. But, this is just the tip of the iceberg. Soon, all financial services firms will need to leverage the power of AI to deliver better experiences, lower costs, reduce risks and increase revenues.

Interestingly, a full quarter of organizations surveyed had no plans to implement any artificial intelligence solution in the next 18 months. Given that 45% of executives by Deloitte thought that AI will be
mainstream in the next 2 years, many organizations may be caught off-guard from a technology perspective.

When the report evaluated the state of AI deployment by the size of organization, it is not surprising that close to half of the largest financial institutions (over $50B) have deployed at least one AI solution, with only 12% of the largest organizations not having it on their roadmap at all.

In lower asset size categories, however, the report found that the level of current and future deployment goes down significantly. “While 25% of regional organizations (assets of $10B – $50B) have an AI solution in place today, far fewer in any smaller asset category are using AI,” states the study. Not surprisingly, the percentage of organizations that do not place AI as a priority at this time increases as the size of organization decreases (with the exception of the very smallest of organizations).

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Types of AI Solutions Used

When we asked financial organizations who had implemented at least one AI solution (or were planning to implement a solution) which solution(s) they have deployed or were planning to deploy, security and biometric solutions were the most likely to be in place or on the short-term horizon. The next most likely functionality to be in place or in the near-term plans were customer support (chatbot/robo-advisor) and personalization solutions. Interestingly, of all of the solutions listed, personalization had the lowest ‘no plans’ response.

In a deeper dive into AI use, just over 40% of respondents found the use of AI for fraud and risk to be extremely important with another 32% saying that AI was very important for this purpose. In fact, fraud, security and biometrics were two of the top three functionalities found to be important to organizations globally.

The second most important functionality for AI was for improved marketing and customer experience. Personalization was considered to be extremely important by 27% of organizations while chatbot customer support was extremely important for just over 20%.

AI Business Drivers

When financial institutions executives surveyed by the Digital Banking Report were asked about the importance of business drivers for AI implementation, the desire to enhance the overall customer experience was by far the most important driver, with over 90% of the organizations stating that this was either ‘extremely’ or ‘very’ important. “This finding correlates with the findings from the , where enhancing the customer experience was the number one trend mentioned,” states the report.

The next most mentioned business drivers mentioned in the AI in Banking report were the desires to reduce costs (74% ‘extremely’ or ‘very’ important mentions) and improve back-office speed and efficiency (74% combined importance mentions). On a weighted average basis, increasing product/service use was the fourth most important business driver boosted by the highest level of ‘very important’ mentions.

When asked about using machine learning for improved marketing and customer experience, , Chief Data Scientist at Deluxe stated, “Artificial intelligence and machine learning are constantly improving, and they promise to continue expanding financial institutions’ abilities to better serve customers and shareholders. Meanwhile, as innovation continues, financial marketers can already use existing machine learning tools to achieve maximum impact from their customer acquisition and retention efforts.”

Leadership and Investment

One of the more interesting findings of our research was the diversity of who is managing AI implementation at financial institutions. Similar to what we found when we researched the implementation of customer experience, we found that many organizations have multiple leaders (cross-functional teams) for AI deployment.

When we evaluated the leadership by size and type of organizations, we saw similar results. As expected, smaller organizations and community banks were more likely to indicate that multiple departments (or teams) were managing AI.

Reflecting the early stages of deployment of AI within all sized financial organizations, the increase in AI investment in the short term is significant. Because of the ‘law of small numbers’ (where % increases can look significant yet the underlying numbers are less impressive). Interestingly, community banks (mid-sized organizations) were the most likely to decrease spending on AI or to have no change in spending.

AI Challenges Going Forward

“The most significant challenge for most financial organizations in the deployment of AI solutions is to have the expertise and personnel to deploy the solutions,” stated the Digital Banking Report. Not surprisingly, the second most mentioned challenge for AI deployment was the structure of data available to build AI solutions.

These challenges are not insurmountable, but they are significant. In most cases, firms will not have the resources internally to address these challenges – especially considering alternative priorities in today’s marketplace. Most firms will need to evaluate a build/buy/partner decision. This is especially evident given that the lack of time and budget were also challenges mentioned.

The vast majority of financial services organizations do not currently have the internal resources available to deploy single, let alone multiple, AI solutions. Not only is available talent in short supply, but convincing talented technology and analytic experts to become an employee of a bank or credit union is not an easy task.

This leaves most organizations with a decision whether to buy or partner with a specialized solution provider to deploy AI solutions. The research found that the answer differed by the solution being deployed. Solutions such as chatbots, biometrics, fraud and voice were usually considered partnering opportunities.

According to , CEO of the BAI, “No matter its might, AI technology remains a tool that waits for us to harness its potential, give it marching orders and put it to best use. Armed with clear expectations and strong strategic planning, we won’t need a computer to tell us that knowing what to do with AI – and in what way – demands intelligence of an entirely different kind.”

Purchase the Report

The  report, sponsored by and the , provides insight into the strategies, tactics, trends and level of deployment of artificial intelligence (AI) solutions at financial institutions globally. Beyond a benchmark study, there is analysis of alternative functionality such as chatbots, voice technology, and personalization as well as recommendations for organizations wanting to build AI solutions.

The report is based on a survey of close to 300 financial services executive worldwide and includes 56 pages of analysis and 30 charts.

You can download an executive summary of this Digital Banking Report or purchase the report by clicking .

Jim MarousJim Marous is co-publisher of The Financial Brand and publisher of the , a subscription-based publication that provides deep insights into the digitization of banking, with over 150 reports in the digital available to rs. You can follow Jim on and , or visit his .

All content © 2017 by The Financial Brand and may not be reproduced by any means without permission.

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