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Be more like a "Day 1" company

Think "small" and "agile" when devising tech use cases

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  • Written by  Alenka Grealish, Celent
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  • Comments:   DISQUS_COMMENTS
Old habits die hard. But banks that adopt a digital, quick-response strategy will strengthen ties with business client. Old habits die hard. But banks that adopt a digital, quick-response strategy will strengthen ties with business client.

While it may seem like Amazon and commercial banks are worlds apart, Amazon’s “Day 1” business model offers key success drivers for banks to consider. The Day 1 model, spelled out in CEO Jeff Bezos’ 2016 shareholder letter, in essence means the company continually operates as a start-up. Also, Amazon—like other digital giants and fintechs—is raising customer expectations. And in small business credit, Amazon is competing directly with banks.

Banks, instead, often operate like Day 2 companies and make good decisions slowly based on a maximum amount of data. While banks cannot completely emulate the Day 1 model due to their critical role in maintaining financial stability and security, and meeting attendant compliance constraints, they can adapt and adopt certain aspects of it. Their challenge is introducing agility and experimentation, while maintaining reliability and resiliency.

Amazon’s Day 1 model rests on four pillars: pursue extreme customer centricity (“true customer obsession” and “desire to delight customers”), resist managing to proxies (a common proxy is process), embrace external trends (machine learning, artificial intelligence), and enable high-velocity decision-making.

Easier and faster

What does extreme customer centricity look like in banking? Banking’s new era of customer centricity is going far beyond product development and marketing to the entire customer journey. Today, a digital strategy must permeate the entire bank. Originally, many banks focused on the front end and new “form factors” (e.g. mobile banking applications). Now, leading banks are seeking to digitize the customer experience and bank operations end-to-end, adopting the approach of digital giants and fintechs that develop their products and adapt their operating models based on digital delivery. While a digital strategy must be spearheaded by the C-suite, it has to bring together stakeholders from the business, operations, and technology sides. Moreover, it needs a strong, relatively new member of the team, data science, which includes artificial intelligence (AI).

The goal is to differentiate based on customer and employee experiences. The mantra is easier and faster: easier and faster to do business with them (customer onboarding, self-service, digitization of manual processes) and to innovate and harness emerging technology (AI, robotics, blockchain).

Supply chain goes digital

An example of delighting the customer is the digitization of the financial supply chain—from procurement and financing to payment and reconciliation. Being a leader in digitizing the financial supply chain is a powerful value proposition. It enables a bank to extend its reach upstream of payments and migrate from being product-centric to being customer workflow-centric. By integrating its services into customers’ workflows, the bank becomes a vital partner and reduces customer attrition. A bank also may generate new revenue-sharing opportunities and attract new customers.

Two trends are converging and accelerating progress in digitizing the financial supply chain. First, numerous players—among them fintechs, enterprise resource planning, and accounting software providers—recognize the operational pain along the chain and strive to remove it. Second, software as a service, cloud computing, and application programming interfaces are facilitating partnerships along the chain. Third parties, however, cannot solve the pain points and scale alone. Banks and third-party providers need each other to realize the potential of digitization.

Embrace bots, blockchain

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Which external trends should banks embrace and how? Two in particular: AI and blockchain. While it is early days for both, they will drive disruption and opportunity in banking over the next decade. Given the multiple revenue and cost pressures banks face, it is becoming imperative that they harness the potential of AI. AI shows strong potential across the corporate banking business and operating model.

Starting with the front office, customer service and relationship manager support are prime targets. A common testing ground is using smart bots—AI-powered software that responds to queries or performs basic, repetitive tasks—to support self-service or drive call center routing, for example. Moving to the middle office, there are numerous pain points, with dominant ones being found in onboarding, compliance, and credit underwriting. Smart bots can be used here to pull financials and file with regulators, or cross-check the OFAC list.

On the credit side, machine learning can improve credit scoring and pricing by processing both internal and external structured and unstructured data. Fraud detection, which bridges the middle and back office, is another prime area for machine learning that shows strong potential in resolving alerts of a suspicious transaction and determining risk level. In the back office, various types of AI can drive process automation and optimization. In payments processing, straight-through processing rates can be increased through auto-correction, root cause analysis, and smart routing.

Blockchain (“enterprise-grade” as opposed to public blockchain like Bitcoin) shows potential in three key areas: cross-border payments (near term), trade finance, and KYC/AML (long term). We are clearly in the experimental, hard work phase. Banks have been busy running multiple proofs-of-concept. As the pain points to solve become clearer, banks, their third-party tech partners, and commercial clients will make breakthroughs and set precedents over the next five years. Likely within a decade, we will see central banks using blockchain and issuing a digital version of fiat currency.

Small, agile works for banks

Exploring cutting-edge technology like AI and blockchain is daunting. While there are a myriad of use cases a bank could pursue, the best approach is to think small and agile, rather than big-bang transformation, and anchor on one or two use cases with the greatest potential return. To determine which to target, consider not only current customer needs and pain points, but the potential to leapfrog the present and deliver a completely new product to meet customer needs. As Bezos states: “Even when they don’t yet know it, customers want something better, and your desire to delight customers will drive you to invent on their behalf.” He points to Amazon Prime as an example. In banking, a classic example of this thinking is the branch. In the pre-internet world, a customer looking for convenience would have expected additional branches with drive-throughs—not conceiving of online banking and remote deposit capture. Some banks rolled out these products ahead of expectations, but most waited until customers requested them. That mind-set is changing.

Leading banks are taking an agile approach to experimenting with new technologies. This typically involves a small product and strategy team whose goal is to assure cross business-unit initiatives. Agile equates to running proofs-of-concept that test and improve on use cases, then undertaking a small number of sprints to launch minimum viable applications for testing (testing with a cross-border, intrabank subsidiary, for example), and then incrementally increasing scope. These teams experiment, develop minimum viable products, fail fast, learn, succeed, and scale, or as Bezos says: “Double down when you see customer delight.”

No “walled gardens”

Agile also requires high-velocity decision-making and developing new processes and protocols, that is, “resisting proxies”—entrenched processes that deter change. This is a big cultural and organizational challenge for banks, which tend to move deliberately. Sometimes they follow that path for risk or regulatory reasons, but not always.

In contrast, Amazon’s formula involves flexibility in decision-making processes, ability to base decisions on “somewhere around 70% of the information you wish you had,” room for “disagree and commit,” and quick recognition of misalignment issues and escalation to a higher level to resolve. Teams may have different objectives and fundamentally different views. No amount of discussion will resolve that misalignment. Without escalation, the default dispute-resolution mechanism is exhaustion—whoever has more stamina carries the decision.

In addition to adapting and adopting aspects of a Day 1 model, banks must overcome organizational and cultural impediments to change and any desire to preserve the status quo, especially in lucrative businesses like cross-border payments. They should evaluate which of their services and processes could be improved or obsolesced by new technology. Furthermore, embracing the future and exceling in innovation requires that banks break from the traditional view of a bank and its products and services as a walled garden, and develop an open banking strategy in which a bank partners with third parties to manufacture and/or distribute better products and services, and embed themselves into customer workflows. As other industries with rich revenue pools have experienced, technological progress will proceed with or without them.

About the author

Alenka Grealish is a senior analyst at Celent. She has more than 20 years of consulting and research experience in the banking industry.

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