Without a doubt, data is one of the greatest assets of any organisation, particularly so in banks. For Emirates NBD, data is being leveraged in such a way that it adds value to both the customers and the business. Predictive analytics, for example, is one area that presents huge opportunities and one that Emirates NBD is truly good at.
Among the most successful programmes the Bank launched is its Multiplier Effect (ME). Emirates NBD’s Multiplier Effect framework is a modern solution to understanding changing customers’ needs, predicting their future requirements, combining these with revenue potential and communicating the next best product or service through their most preferred channel.
The three pillars of the ME are – propensity, profitability, and personalization. This concept provides a holistic framework in shaping the next best opportunities for the bank by enabling the prediction of customers’ needs with utmost accuracy while also maximizing the bank’s revenue thereby propelling them to the next stage of evolution.
But it doesn’t stop there. Emirates NBD is also smartly using technology and customer intelligence to realize noble concepts such as “personalisation at scale” and “customisation in all aspects of life.”
These efforts by Emirates NBD have been recognised at the recent Middle East & Africa Retail Banking Innovation Awards 2021 organised by The Digital Banker where they won the awards, Best Customer Centric Business Model, Best Data Analytics Initiative, Best Social Media Marketing Initiative and Credit Card of the Year.
“Emirates NBD has adopted a state-of-the-art approach in delivering value to its customers. Leveraging artificial intelligence, data analytics and machine learning, the Bank has been able to meet changing consumer demands and facilitate value creation like never before,” said Nirav Patel, Managing Director at The Digital Banker during the awards ceremony.
Fighting Money Laundering through Data Analytics
Money laundering and financing of terrorism have been topics of great concern for regulatory authorities and financial institutions, worldwide. According to McKinsey, up to USD 2 trillion is laundered annually through the global banking system, which roughly translates to 2-5% of the global GDP. However, identifying and assessing risks associated with money laundering is a tedious process further complicated by a host of legacy issues, regulatory requirements, and subject sensitivity.
To deal with such complexity, regulatory authorities advise banks and financial institutions to adopt risk-sensitive approaches in identifying ML/FT risks.
To tackle this issue, Emirates NBD has developed a scientific risk rating methodology where vast amounts of data from various systems were combined and analysed to design a statistical scoring framework that addresses the risk assessment requirements at different stages of the compliance lifecycle.
Application Scorecard
As the name suggests, the application scorecard assigns a score to the customer at the time of acquisition based on information captured in the application form and other documents collected from the customer at the time of onboarding. The score reflects the compliance risk associated with the customer at the time of onboarding. The 150 attributes available at onboarding were used to create composite features which are more powerful predictors of customer behaviour.
For instance, the initial deposit was combined with industry risk to compare the deposit amount of a customer with their peers in the same industry. If the deposit is not in line with the industry peers, the customers were penalized while assigning the score. A combination of raw and composite attributes was then used to design a multivariate scoring framework. Once each customer application is entered into the system, it goes through this framework that assigns a final score based on multiple scoring attributes.
Behaviour Scorecard
A Behaviour scorecard is used to assess the compliance risk of existing customers. Compliance risk of existing customers can be better gauged by assessing their behaviour/relationship with the bank post onboarding. For this reason, multiple factors related to account/transaction behaviour were analysed along with the static attributes. Attributes such as the amount of cash deposits, annual turnover, amount/frequency of transactions in high-risk countries, unsatisfactorily closed alerts from other transaction monitoring systems were added to the model. Many composite attributes were also defined. For instance, a customer’s total amount of transactions was compared with their industry peers to identify outlying patterns. A variable that combines the number of alerts and transactions in high-risk countries was also incorporated into the modelling base. 500+ raw attributes and ratios covering all aspects of a customer’s relationship were added to the modelling base and Logistic regression was used to formulate a final modelling equation that assigns a behaviour score for every customer based on final model attributes.
All the models were vetted by KPMG, a third-party consultant prior to implementation. To ensure that the models meet global standards and are fit for use, KPMG was brought in to independently evaluate the models. Models were evaluated quantitatively and qualitatively and found to be fit for use.
Personalisation at Scale
“Personalization at Scale” is one of the key pillars of the 5R Marketing Strategy at Emirates NBD (Right Customer, Right Channel, Right Time, Right Offer and Right Message). To deliver the ‘Right Message’ that would resonate with its audience, various elements of digital communication had to be understood and personalized for NBD Emirates’ customers.
To achieve this, the Bank combined its marketing messages with an industry-leading machine learning platform PERSADO, to create more than 350 variations of its creative assets and effectively reach out to a wider customer base with relevant messaging.
Prior to this initiative, a key challenge for the Bank was to effectively add a personalized touch that would resonate with its audience’s myriad interests.
Upon implementation, the team had seen a significant uptick in its performance metrics. For example, CTR (click-through rate) was up 43% while leads registered a 68% increase. These figures were complemented by a drop in CPC (cost per click) and CPL (cost per lead).
And while personalisation works for Emirates NBD’s advantage, partnership also works well in equal measure.
In partnership with Visa’s Digital Benefits Platform, the Bank has launched the Emirates NBD Flexi Visa Credit Card. The card was launched keeping in mind the constantly evolving needs of the customer. The main differentiation of this product is that Emirates NBD, along with Visa, utilises a range of APIs to integrate with benefit providers.
By integrating merchants with APIs, the Bank can offer a wide range of benefits with real-time eligibility checks. Cardholders just need to select their benefits on the product portal and when they choose to use a benefit, they just need to swipe their card or type in their card number on the merchant’s website. The APIs will check if the cardholder has selected the benefit and allow access if so – a seamless process that many customers appreciate.
While the product is still in its early stage, key indicators are quite encouraging. Approximately 70% of the cards are spend active and have an average monthly spend of $ 2,200, which is already 1.2x more than the similar card variant.
Driven by changing consumer demands and an intense focus on personalisation, Emirates NBD is astutely carving a niche in the industry as a bank that can provide customisation in all aspects of its customers’ life.
