Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
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DBS Bank is a Singaporean multinational banking and financial services company. The bank has over 150 branches in Singapore alone and by total assets, DBS is the country’s largest bank.
Per the bank’s 2021 Annual Report [pdf], DBS earned approximately $6.8 billion in net income on $14.3 billion in revenue. DBS Bank is owned by the DBS Group, which has a reported market capitalization of approximately $61.6 billion.
The bank employs 33,000 globally and trades on the Singapore Exchange.
DBS is unique in the still-conservative banking industry in that its various technological innovations have garnered worldwide admiration, including from prestigious institutions like MIT and Harvard Business Review. The bank has acquired multiple awards and accolades for its design and implementation of technology, including AI and machine learning initiatives.
In this brief primer, we intend to examine two use cases showing how AI initiatives currently support DBS’ business goals:
We will begin by examining how DBS Bank has used machine learning to improve the end-to-end recruiting cycle and save company resources.
DBS partnered with impress.ai (“Impress”), a Singaporean software development company. Impress develops AI-powered recruitment software.
Impress offers an AI-powered chatbot that purportedly automates and streamlines the end-to-end job application process by pre-screening and assessing candidates and answering interview- and job-related inquiries.
Per DBS’ website, it sought a solution to automate “pre-interview questions and tests that previously had to be carried out at face-to-face meetings.” The bank cited the following statistics and information that may have served as an impetus:
The resulting solution for DBS, JIM for “Jobs Intelligence Maestro,” was customized to DBS’ hiring process. Available resources are unclear on the data used in this process, though DBS lists the process on its website.
According to the available information, JIM is multifaceted and appears to include the following recruitment capabilities:
In an interview with the website HKatha, the head of HR at DBS Bank, India, Kishore Poduri, states that the platform has expedited the time taken to assess a resume. In a value proposition video for the software (see cited clip below), the recruiter workflow appears to be as follows:
On its website, Impress states that its resume-screening process uses algorithms to evaluate candidate resume data against recruiter preferences (likely referring to the “competencies” mentioned earlier.)
The following 1 min, 36-sec video conveys the value proposition for the impress.ai platform:
Following resume upload, it prompts the candidate to complete a pre-screening assessment. According to the long demo video cited at the bottom of the article, the assessment takes about two to three minutes to complete.
Recruiter preference data trains the platform to score a candidate’s resume, pre-screening questions, and assessment. Per the case study cited earlier, it seems that the output of this process is a “high-potential shortlist” of job candidates. In this way, the candidate data is most likely filtered and presented to the recruiter via their ATS.
According to Impress’ product webpage, the client user making the hiring decision can monitor and manages the recruiting process from their platform dashboard.
According to the case study [pdf] published by Impress, the platform integrates with an Applicant Tracking System (ATS). Impress lists three ATS integrations on its website: Taleo, Workday, and SAP Success Factors. This integration apparently syncs assessment scores and candidate reports from JIM to the ATS.
A longer 12 min, 6-sec video follows, which is a full demonstration of the software. This video is handy if you desire the candidate’s perspective:
Since its implementation in 2018, DBS reports that the solution has saved work time equivalent to 40 hours per month. Per the case study cited earlier, Impress reports that its platform:
Ever since the Bank Secrecy Act of 1970 became the law of the land in the U.S., a ripple effect led to banks globally being held liable by their respective governments for preventing money laundering.
Traditional transaction surveillance is bound by a rules-based system that assesses transaction data throughout the bank. Transaction surveillance typically involves monitoring customer deposits, transfers, and withdrawals.
The technology works by passing all transaction data to the rule-based system. A human then screens and flags for analysis data matching the conditionals set by the system.
The difficulty with this system is that it creates a massive number of false positives — up to 98 percent. For the business, this means more company resources — human and monetary — are directed toward sifting through reams of data.
According to an interview from The Straits Times with Head of Group Investigation at DBS Bank Elvin Lim, company leadership developed AI and machine learning capabilities in these processes to identify the transaction usage patterns of customer accounts using all available data points.
MIT Press published a book by university professors Thomas H. Davenport and Steven M. Miller called Working with AI: Real Stories of Human-Machine Collaboration. This book detailed the DBS case study, among others.
Per Davenport and Miller, DBS’ new model combines the traditional rules-based system with AI and machine learning capabilities. The result was an augmented system capable of:
They integrate the model with a user platform that can consolidate transaction support data from across the bank and present it on the analyst’s screen.
Prior to model operation, DB analysts would spend two or more hours investigating an alert. After integrating the new model with a platform that incorporates outputs of the rule-based engine, DBS can resolve ⅓ more cases in the same amount of time.
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