Contextual Payments: Overview
In banking, the physical branch is no longer at the center of customer engagement. The center is the consumer. Contextual banking involves shifting to customer-centric models. These models use data to provide “personalized contextually relevant experiences.”
What is Contextual Banking
- Contextual banking recognizes that, like other consumer behaviors related to mobile technology, banking is no longer an event that must be planned, but is a fluid, interactive activity that can take place anywhere and anytime.
- This shift means new demands on the bank’s existing technology and data management. Banks must go beyond providing a mobile app and become partners in the context of customer’s lives. Adding value to the customer in any setting they choose needs to become the primary goal of a successful bank.
- A recent study by Forrester found that while larger banks have upgraded and streamlined their transactional features in mobile environments, only three in eight have adequate content; only two “support external account aggregation; and none provide contextually, actionable financial advice as often advertised by executives.”
- Contextual banking, therefore, requires an in-depth knowledge of the consumer where personalized experiences are integrated into the ongoing customer journey. In contextual banking, content and recommendations are based on consumers’ personal choices and even location.
How to achieve contextual banking
- The success of contextual banking depends on data — how it is structured, how it is managed, what is accessed, what it is used for, and more. Banks cannot restrict themselves to their data but must look outside their data warehouses to enterprise-level data and even data outside their control. With customer permission, accessing and aggregating data become a key feature of customer service in contextual banking.
- A comprehensive data strategy allows banks to provide the contextual recommendations their consumers want, “at the times and in the environments most appropriate for those customers to take actions that drive desirable outcomes.”
- The ultimate goal is for banks to integrate their data strategies with many applications to provide not just a banking app, but a comprehensive customer experience platform that underpins an ecosystem of providers, services and end-users.
- Ultimately, banks can integrate their data strategies with diverse applications to create not only another banking app but a different customer experience platform that supports an ecosystem of services, providers, and end-users.
- The more experiences that banks provide on these platforms, the more customers become engaged and appreciate the relevancy of the bank’s products and services. As the experiences expand, banks can not only generate more revenue but get more customer data that can be leveraged to provide further personalization. This continuous business cycle strengthens the bank’s role in the financial needs of its customers.
- The data strategy must also support digital marketing, which requires a clear understanding of how to optimize each stage of the sales funnel. Most customers are already comfortable looking for information on web sites. It now becomes important to be able to convert these inquiries into sales.
- Leading banks are already using both first and third data, such as geospatial and browsing behavior, omnichannel campaigns, a robust marketing stack, and agile operating models. A North American institution with these elements deployed tripled their annual online product sales in one year.
- Digitally native companies in other sectors such as ecommerce, entertainment, fashion, and travel have been successful in providing personalized experiences for their customers. So too must banking.
- Individual customer data has exploded in recent years. Converting this data into relevant offers for consumers at the right time becomes the challenge. One example is providing location-specific offers at the moment the customer enters the coffee shop, the grocery store, or movie theater.
Why it is important
- Customers want banking interactions that are simple, intuitive, and connected across physical and digital environments.
- Even more importantly, a recent McKinsey analysis reported that banks with the best customer experiences had “meaningfully higher deposit growth over the past three years.”
- This growth is coming from attracting new customers and by deepening their relationships with their current customer base.
- Research has shown that highly satisfied customers are 2.5 times more likely to open new accounts or buy new products with their existing banks than customers who think their bank’s services are just OK. These satisfied customers are also less price-sensitive and more inclined to generate positive word of mouth.
Contextual Payments: Case Studies
Two case studies of banks implementing contextual banking are provided. The first is a B2C case study of DBS Digibank. The second is a B2B case study of an anonymous large US bank which implemented a contextual payment process for its corporate customers.
1. B2C Case Study — DBS Digibank
- DBS is one of the leading financial services group in Asia, with over $320B in assets and 280 branches across 18 markets.
- DBS has spent the last three years deeply immersed in digital change, including leveraging big data, biometrics, and AI to make banking seamless and straightforward for its customers.
- Using an AI tool that is an expert in banking, DBS ensures that conversations are accurate and contextual. The tool includes core banking statements and financial services data, including accounts, transactions, and payments.
- Because the platform DBS uses was explicitly created for banking services, the assistant can understand and answer financial and banking queries.
- The tool is also customizable. When it was first implemented, it could answer 1,178 unique questions about Digibank’s customers, products, and services.
- It was also localized for different markets and different languages.
- It provides a contextual banking experience by processing banking data in real-time.
- For example, when a customer indicates they want to make a payment, the system can determine to whom the payment is being made and how much the payment should be. It does this by tracking the customer’s past behavior.
- The tool has proven to be highly accurate. It has exceeded expectations and is continually improving using machine learning.
- The tool DBS uses has “a combination of supervised and unsupervised learning strategies to tune and train statistical models.” It uses annotations to target and process new questions and answers required.
- The system also uses the annotation tool to work with live subject matter experts to update the system continuously. The tool has a customer portal to allow staff to update questions and answers, taxonomy, and frequently asked questions content to improve the user experience continually.
- DBS required a fifth of the resources of a traditional bank.
- Contextual banking has enhanced DBS’s reputation as an innovative bank.
- The AI system drove down costs by handling 82% of all customer requests. Only 18% asked to transfer to a live operator.
- The conversations are engaging, and a typical interaction includes “six exchanges with the customer sending at least three messages.” The assistant responds with human-like conversation
- DBS sales have increased.
- All customer conversations are digitally captured, cleansed, and anonymized to support compliance requirements while also allowing data mining and audits.
2. B2B Case Study — iGTB Anonymous US Banking Client
About the Bank
- This anonymous client of iGTB is the fourth largest bank in the US.
- It has a global footprint and offers retail, corporate and transaction banking.
- The asset size is over USD 1.5 trillion.
- Through organic growth and acquisitions, the bank had over 60 different platforms and databases for core banking, liquidity, global trade, and global corporate services.
- The bank was unable to check real-time balances and credit limits for outgoing payments across regions.
- This inability caused challenges for their business customers, both small and large. They needed to see immediate balances and their customers needed to verify payments.
- This technical solution was transparent to their customers.
- The bank designed and developed a funds control system branded internally as RBCS — Real Time Balance Checking System.
- This system interfaces with multiple legacy systems using an Account Server.
- It extracts limits data via a credit engine.
- The system takes into account FX Rates, Balances, Limits, Cut offs, etc. and authorizes or declines payment.
- The system was benchmarked for auto-retry at 60,000 transactions per hour and online transactions at 32,000 transactions per hour.