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#AI
#EmotionRecognition

How might we accurately identify emotions of self-service customers, so as to better respond to and engage with each customer?

Background/Context

The COVID-19 pandemic has accelerated the adoption of digital banking; it is estimated 78 percent of Indonesians now use digital banking actively. Bank Central Asia (BCA) digital transactions also grew consistently and significantly 51% in 2021 compared to the prior year. Their customers can transact on the online platform, BCA Mobile, KlikBCA and myBCA, as well as on ATM and other self-service machines at the digital branches.

Whilst the shift to digital banking helps BCA serve their customers more efficiently, it removes physical interactions between bank staff and customers. Good customer service is founded on empathy; BCA relies on a human approach to understand the customer's emotional state in order to adapt quickly to provide a more personable service. The lack of physical interactions with self-service customers makes it more challenging for BCA to keep providing good service.

Objective

How might we accurately identify emotions of self-service customers, so as to better respond to and engage with each customer?

Solution

BCA is looking for an AI solution that:

  • Observes a range of emotions in real time, which may include accurate predictions based on the customer's behavioural responses, such as speech pattern, facial expressions, etc.

  • Detects the mood, such as angry, happy, in distress, etc.

  • Ascertains the possibility of fraud through lie detection in some situations.

With such a solution, the bank can be more responsive to customer needs and deliver quality service, even if the customer is not in the physical presence of BCA staff. BCA believes this can also improve customer satisfaction.

View Other Participating Demand Drivers Challenge Statements

#IoT#Traceability

How might we monitor and track assets accurately, seamlessly, and in real time?

Background/Context

Bank Central Asia's (BCA) is responsible for millions of customer assets across their entire system and in a large number of locations. These assets are mostly high-value or high-importance items that are placed in the bank for safekeeping. They range from precious items, such as money, computers, laptops, hardisk, etc.

Currently the assets to be tracked are tagged and scanned. Thus, this system is dependent on manual inputs and prone to human errors, such as keying in the wrong information during tagging or not updating the system when an item is moved. Another tracking solution currently being implemented, uses universally unique identifier (UUID) IoT beacons. As these beacons are powered by batteries, there could be a lapse in the tracking if the battery runs out.

Objective

How might we monitor and track assets accurately, seamlessly, and in real time?

Solution

BCA is looking for an asset tracking solution that improves on the current beacon system, and:

  • Ideally requires little, if any, inputs from the staff handling the assets

  • Gives the exact location of any asset being tracked

  • Requires minimal maintenance by BCA

  • Can operate in an environment that may not have Internet access (or have continual access)

The solution should improve operational efficiency, as staff spend less time and effort to track down the items. Moreover, as BCA is entrusted with the safekeeping of these valuable items and documents, knowing exactly where each asset is means delivering on that trust.

View Other Participating Demand Drivers Challenge Statements