Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed

Abstract

As a research field, artificial intelligence (AI) exists for several years. More recently, technological breakthroughs, coupled with the fast availability of data, have brought AI closer to commercial use. Internet giants such as Google, Amazon, Apple or Facebook invest significantly into AI, thereby underlining its relevance for business models worldwide. For the highly data driven finance industry, AI is of intensive interest within pilot projects, still, few AI applications have been implemented so far. This study analyzes drivers and inhibitors of a successful AI adoption in the finance industry based on panel data comprising 22 semi-structured interviews with experts in AI in finance. As theoretical lens, we structured our results using the TOE framework. Guidelines for applying AI successfully reveal AI-specific role models and process competencies as crucial, before trained algorithms will have reached a quality level on which AI applications will operate without human intervention and moral concerns.

Ready or Not, AI Comes— An Interview Study of Organizational AI Readiness Factors

“…This comprises equipping employees with AI-related know-how such as statistics, data management, data analytics, or data engineering on the one hand, but also with domain know-how on the other hand (Pumplun et al 2019). Owing to a shortage of skilled AI specialists in the labor market, it becomes obligatory for organizations to invest in upskilling employees ' capabilities (Davenport 2018;Kruse et al 2019).…”

“…Innovativeness is based on the amount and pace of adaptability that organizational members possess (Kruse et al 2019). Innovative behavior with a general-purpose technology such as AI requires employees to initiate change at a rapid pace and in many areas so that organizations can realize AIs' full potential.…”

“…Data availability comprises the relevant amount and types of data which are both necessary for AI models to be trained and to generate accurate predictions (Agrawal et al 2018;Kruse et al 2019). Regarding data type, interview experts point out that different data types influence AI readiness.…”

“…AI-relevant data quality dimensions include, for instance, completeness and correctness (Sidi et al 2012). Since organizations often face data quality issues with historical data, they need to improve on universal capabilities like data preparation, data processing, and data quality assurance to increase data readiness (Groopman 2018;Iansiti and Lakhani 2020;Kruse et al 2019).…”

Assessing Factors Affecting Intention to Adopt AI and ML: The Case of the Jordanian Retail Industry

“…It has been argued in the study of Alarie, Niblett and Yoon [1] that the advent of artificial intelligence has shaped the way in which companies operate. However, the adoption of artificial intelligence is still a challenging factor for most of the companies in the business environment [12]. It is due to the reason that there are various barriers for the adoption of artificial intelligence which restricts the companies for the purpose of effectively implementing the artificial intelligence [18].…”