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4 Digital Transformation Trends in Corporate Banking for 2022

4 Digital Transformation Trends in Corporate Banking for 2022

The recent global crisis spotlighted retail banking, but similar forces are at work in the corporate banking sector. Out of sight often means out of mind. However, the jolt for corporate banks overly reliant on paperwork and branch dependencies is real.

For corporate banks, the need of the hour is to pivot to new mindsets and create partnerships in the fast-evolving ecosystem influenced by pure-play B2B ecosystems (Paysme, Tinkoff, Stripe) and BigTechs (Amazon). It will be essential to bounce back from pandemic aftershocks and transit to banking models that are experiential, sustainable, and inclusive.

2022 Corporate Banking Landscape.

Complicating the turbulence are hardly new factors – stringency in regulation, shifting millennial client needs, digital revolution, and globalization. Unsurprisingly, Bill Gates spoke of banks as dinosaurs almost three decades ago.

In times of shrinking lending margins, increased cyber security breaches, tech-savvy customers, and the fast-and-furious inroads of Fintechs, corporate banks must leverage big data and scale their advanced analytics capabilities. We are past the tipping point; 2022 is the turnaround year.

  1. Customer experience is now business of experience: Consider this August 2021 report. 97% think banking apps as a customer service tool must have a place in corporate banking. 66% say yes for chatbots and virtual assistants, and 72% for mobile wallets. The dire need for corporate banks to simplify their business processes using intelligent automation is brought home by a sobering stat: 1 in 5 millennials plan to bypass corporate work and start enterprises. Be it the digital-first mindset, mobile-friendly attitude, their businesses having international components, or the information-hungry behavior; corporate banks must pivot from banking to the business of experience. After all, commercial clients using digital services for their personal needs know how vital seamless and experiential journeys are. Given the heightened intolerance levels, it is a plot that corporate banks cannot get wrong. Here’s the evidence: An October 2020 survey showed that in the previous 12 months (coinciding with the pandemic’s peak), 16% of SMEs switched banks against the industry average of 10%.
  2. From traditional supply-chain finance to ecosystem platforms and B2B super apps. Marketplace ecosystems enable geographic expansion and create new business models and sources of finance. With a Banking-as-a-service platform (open banking), leading organizations (Citi, DBS, for instance) are leveraging APIs to open data and services to third parties. For example, BBVA revamped its supply chain finance solution, allowing third parties to retrieve business user balances and transactions in a market standard format. Unlike stand-alone operating banks relying only on the internet with limited solutions (supplier finance and distributor finance), ecosystem platforms bring in broader market players (membership banks, trust companies, and multiple corporates). The shift is in technology (blockchain included) and business models (trading platforms, client brokerage, and IT solutions services). The People’s Bank of China recently coordinated a new platform to leverage blockchain technology. The outcomes? Improved capital efficiency and surge in the financing, not to mention the scope of future innovations.
  3. Back to Basics with Open Banking: Be it achieving operational excellence, customizing product proposition, or leveraging data and analytics, corporate banks in 2022 will adopt a ‘back-to-school’ approach. Scrutinize manual processes and then understand customer expectations by looking at the tools and products they use; corporate banks can then move to test the stability and security of their services (front-office and back-office ones). Why is this important? Because as open banking lifts off, the revenue opportunities ($9.6 Bn, 2022) and the SMEs that will adopt it (71%) are simply unmissable. Even so, open banking allows better tracking of business performances and simplifies accounting, payroll, and auditing.

What about the other drivers? For one, boosting operational excellence (including sales force enablement and process digitization) will improve cost structures and customer experience. Secondly, customizing product propositions will facilitate sector-specific solutions, and leveraging data and analytics across pricing, selling, retention, and prospecting will augment corporate banks’ revenues.

  1. Accessing Automation: As OCR technology enables corporate banking to beat legacy systems and paper-based documentation, automation’s role in 2022 will only grow. Simplifying applications, automated underwriting, dynamic documenting, e-signing, and paperless transactions will be the various transition points to a bank’s touchless process. A recent study by Dun & Bradstreet reports that 36% of businesses had already begun to automate specific tasks.

Pre-automated techniques from the front office, central processing, and authorizations must transit to AI-automated processes. Per March 2021 KPMG CEO Outlook Pulse Survey, 74% of business leaders report that digitization of their operations and formulation of the next-generation operating model has picked up from 50% (as reported in August 2020).

Conclusion

As corporate banks take on multi-headed challenges – upended trade finances, risk in cash-flow crunches, complying to newer sustainability standards, and meeting head-on the growing threat of B2B super apps – their solutions would encompass data mining, incorporating analytics in processes, and adopting ‘as-a-Service’ models.

2022 as a year will test incumbent banks for their transformative mindsets. At stake are resiliency and relevancy.

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Digital Operations in Banking: Advantages & Challenges

Digital Operations in Banking: Advantages & Challenges

Here is a story of a bank from the recent past that foretells the industry’s immediate future. Especially about digital operations in banking.

In June 2017, a mobile-only bank came into existence in South Korea. In its first 24 hours, 300,000 subscribers signed up. Within two weeks, it had surpassed 2 Mn customers. At that point, the bank had clocked $930 Mn in savings, and lent out $710 Mn. In 2 weeks, Kakaobank was in business!

As of today, in a country of 50 Mn people, with economically active population of 25 Mn, Kakaobank has 10 Mn + customers.

Be it any industry, it is obvious that orbit-shifting innovation precedes meteoric growth. But what is not obvious, are the fundamental questions that start it all. In the case of Kakaobank, these questions were: “What is the purpose of a banking business?”, Why sell loans, and the other products? Are we in this business for the money?”

Digital operations in banking are both about clarity and differentiation.   

Today when every other tech. company, social media, or an e-commerce company aspires to become a bank or a variant, a key question pops: is the sector more important or is the customer? The answer comes from the art of thinking clearly, and thinking for the long-term

Kakaobank’s vision emerged bit by bit. They didn’t want to beat their rivals’ rates. They wanted to transform banking by making it totally customer centric. Unlike traditional banks where 60% of outgoings are spent on branch operations and back offices, Kakaobank passed the cost differential to customers from day one. How? Through tech. assessment, risk control, customer experience design, the digital native offered on-demand services through mobile (not even online).

Current outlook.

The 2021 Global Banking Annual Review talks about how legacy financial entities will have to battle declining profits by 20% – 60% before 2025, if they fail to go digital. In fact, a HBR article in 2014 spoke of how 85% of US retail transactions had gone digital.

As if new market players, new regulations, and new technologies that disrupt customer’s expectations and perceptions weren’t enough; the recent virus crises, has brought in more complexities. Not only did digital banking accelerate, but also the use of cash fell, and sustainability and circular economies grew in mass opinion. All these factors have a direct impact on how banks make and spend money, and how their brand is perceived.

Not a question of ‘if’, but ‘when’, as legacy banks embrace varying degrees of digital operations, let’s understand the pros and cons that come with it.

Digital Operations Solutions in Banking – Advantages.

  1. Boost revenues, lower costs

Basis McKinsey’s executive AI playbook for Banking value, the potential annual value for AI and analytics (both traditional and advanced) is $1 Tn. Digital operations increases personalization exponentially, generates efficiencies via higher automation, reduced error rates, and better resource utilization.

  1. Enhancing customer experiences through uncovering new opportunities

Be it front office (biometrics, natural language processing for doc. scanning, humanoid robots in branches, and conversational bots) or back office (machine learning to detect fraud and cybersecurity attacks, real-time transaction analysis for risk monitoring) leading financial institutions are embedding more and more robotic process automation, virtual interfaces, and machine learning techniques, into their processes.

  1. Beyond retail, digital operations benefits small- or medium-enterprise customers

Today’s mid-size enterprise customers directly benefit from a bank’s digital operations. Here is how: from receiving customized lending solutions, to micro-expression analysis that reviews loan applications, to seamless inventory and receivables management, to being provided with a SME platform to source buyers and suppliers, and getting support in off-banking hours (by AI-powered virtual advisors)

Digital Operations Solutions in Banking – Challenges.

Digital transformation for legacy finance entities is not very different from flying an aircraft and repair it in mid-air at the same time. Consider their predicament: On one hand they are asked to operate with the nimbleness and speed that comes to Fintechs naturally, and on the other, they cannot sacrifice the rigorous demands of scale, security, and regulators.

In fact, across industries, graduating to become ‘AI-first’ or ‘totally digital operations’, calls for a strategic mind shift and putting in place foundational building blocks.

For banks though, the absence (or inadequately functioning) digital operations reveals immediate challenges.

  1. Lack of scale leads to sub-optimal efficiencies

Core systems if not modernized are simply not powerful to operate upwards of 150+ transactions/second. Along with the significant time, effort, and team sizes needed, it takes a long time to provision development and testing environments.

  1. Poor accuracy with lower customer satisfaction scores

High error rates, poor refresh rates, data being silo-trapped and difficult to retrieve, and process are all challenges that mean there is no single source of truth. Moreover, the system is ill-positioned to integrate with external sources, leading to timely (and costly) workarounds.

  1. Longer time to market

Limited software and code reusability across internal teams, and the difficulty in collaborating with external partners, results in poor user experience. When data and services are hard to stitch across functional siloes, legacy players find it difficult to combat market complexity. The result of limited coordination and cross-team testing is longer time to market (cost leakages).

To overcome challenges that prevent digital operations from going organization-wide, banks must, one, invest in transformational capability stack and two, aligning that stack for value creation.

Learnings from Kakaobank.

As the Kakaobank example instructs, the four indicators of successful digital banking operations are, profitability, personalization at scale, omnichannel experience, and speed that comes from innovation.

 

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2022’s Go-To Guide to Data Analytics in Banking & Financial Services

2022’s Go-To Guide to Data Analytics in Banking & Financial Services

Compounding high costs of bad data are the opportunity losses banks risk with slow efforts to scale their Data Analytics function.

Not as a set of discrete projects, data analytics must evolve into an actual business discipline. The imperatives to do so are the twin drivers: advancing technologies (the exponential growth in meaningful data and available computing power) and enormous economic pressure banks face today.

Three ways data analytics generate an increase in bank’s profits.

  1. Amplify P&L levers (accelerate growth, enhance productivity, and improve risk control)
  2. Find new sources of growth (creating new business models, e.g., offering data analytics with others in the partner ecosystem)
  3. Deliver on the promise of a digital bank (enhanced omnichannel experience at lower costs)

Before analytics is applied to structured or unstructured sources, financial organizations have to resolve industry-specific challenges (regulatory requirements, data security, data quality, and data siloes).

Today leading banks leverage the power of analytics in more ways than one. One uses machine-learning algorithms that predict currently active customers who might drop business. Another use is to analyze competitor campaigns that curb any unnecessary discounts banks may be offering. Yet, another uses analytics to parse big data to discover microsegments in its customer base to create that next-product-to-buy.

2022 priorities for getting more out of data analytics investments.

  1. Post-crisis, as banking analytics use-cases increase across sales & marketing, HR, Risk & Compliance, and IT, banks will get more bang-for-their buck as they align analytics priorities to strategic vision.
  2. The second boost would come when managers scale analytics pilots by augmenting technical production and engineering capabilities. To succeed would mean to absorb data-driven iterations into work rhythms, something that change management programs can help in.
  3. The third priority concerns staffing. Individuals chosen for analytics roles (data engineers, scientists, ML engineers, e.g.) must bring a collaborative mindset.
  4. Financial organizations will

create value beyond the logical use-cases (digital marketing, transactional analysis, cybersecurity) by exploiting rich data sets by synching data across organizations and finding innovation breakthrough areas.

All said and done, much of these priorities would be possible when banks use robotics to eliminate 20% – 40% transactional accounting work. Not only will this allow finance teams more time for decision-making, but it also helps them gauge how best predictive analytics meshes with the performances they seek.

Banking analytics as it plays across the selling process.

Senior managers tasked with banking operations, and profitability must step back from the customer life cycle to tease out interlocks where analytics brings information and value. It is discussed below with the corresponding analytics benefit.

  • Customer Identification and acquisition (acquisition analytics and campaign design)
  • Customer relationship management (managing portfolio and meeting transactional needs)
  • Customer cross-sell (need analysis, demography, credit history analysis, next-best-product)
  • Customer retention (churn prediction, lifetime value modeling)
  • Customer value enhancement and increasing wallet share (behavioral segmentation, product affinity modeling, and differentiated pricing)

As the saying goes, “Future is already here; it’s just not evenly distributed.” Banks will need to smooth out customer road maps so that each one receives high-quality and personalized relationships.

How does all this tie-up to 2022 predictions for banking data analytics? 

A recent study of 10,000 companies reported that 71% are in the midst of or stand at the edge of disruption. On a 0 to 1, banking moved from 0.43 in 2011 to 0.52 in 2019. In 2020, 1 in 5 banking and payments sectors players were less than 15 years old.

The reasons are familiar – disruptive Gen Y expectations, Fintech entries, accelerated digital banking, and shifting regulation.

Given the mandate – ‘transform or make way’ – no two banks would (or should) approach data analytics the same way.

Practical steps for chasing analytics transformation and the next frontier.

The reasonable steps will remain the same as before: identify business problems, centralize data, automate processes, focus on decision making, optimize finance cycles, fight for talent, and drive continuous improvement. The innovators will turn their data analytics engines in pursuit of three strategic objectives:

  • Reinforcing the core (augment existing core bank offering)
  • Creating a new distribution channel (becoming a preferred partner to third parties)
  • Launching innovative ventures (develop new businesses and business models)

Conclusion.

In the final analysis, as the fog lifts over the current crisis, data analytics in banking will pay dividends for players that use it for intelligent pricing, selling, retention, and intelligent prospecting.

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Top 5 Core Banking Software Companies in 2022

Top 5 Core Banking Software Companies in 2022

As neo-banks win market share and serve customers at around one-third of the cost of traditional. Then, there are threats posed by big tech players eyeing lucrative niches in the value chain. Post-pandemic customer is more digital, and less loyal. Also, today’s banking regulations are equally about innovation (PSD2 for instance) as they are about reporting and compliance.

That being the landscape for core banking software, what are the incumbent banks to do?

The answer, more than ever, is to exploit disruptive technologies like cloud, mobile, big data, microservices, open API’s and AI.  But these technologies rely on a robust banking core.

Before we look at the top 10, let’s see the top 4 parameters that distinguish the great core banking software from the good.

  1. IT cost reduced: Removing technical debt, pushing for higher efficiencies by leveraging cloud-based services, not to mention higher developer productivity (via automation tools DevSecOps) – are all factors that add to reduce costs.
  2. Quicker time to market: Employing both hyper parametrized configuration and higher standardization levels, banks leverage automated testing – all of which helps them to speedily develop new products and services.
  3. Customer-centric proposition: Data differentiates. Modern-day core banking software support integrated data sets and operate on a single source of truth. This capability in turn helps create real-time personalized experiences and brings into play, advanced analytics. Ergo. Precise decision making and a happier customer.
  4. Mastering scale through ecosystem partnerships: Modular architecture and API communication enables rapid scaling. This becomes critical especially in a development environment where core and ancillary services are relatively cheaper.

Little wonder that a McKinsey report, highlights 65% of banks surveyed exploring next-gen core banking platforms.

How were the top 5 arrived at? Simply, by applying these five comparisons.

  • Faster access to support digitization
  • Flexibility in pricing
  • Facilitating faster time to market
  • Supporting agile and remote working
  • Allows new capabilities like open banking

Top 5 Core Banking Software in 2022

  1. Temenos Transact: 25 years of industry-defining banking software, Temenos supports retail, corporate, wealth, and treasury lines of business. 41 of the top 50 banks use Temenos Transact. It has been ranked #1 for 16 years by IBS intelligence. Cloud-native, Cloud-agnostic, API-first, and AI-enabled, Transact (it was called T24), allows banks to launch products in cycles that are 10X faster.
  2. Mambu: Riding on a composable approach (traditional providers lock functions like decisioning, reporting, analytics into dedicated applications), Mambu offers low-code, born-in-the-cloud functionalities that lets banks scale on their terms. With deployment across 65 countries, 200 + global customers, and a 4X higher NPS score than others, Mambu’s USP is to deliver great modern financial experiences.
  3. SDK Finance: With 15 years in FinTech, SDK’s use-cases target neo banks, E-wallets, currency exchanges, and online payments. The core banking software allows creation of digital banking solutions so financial institution can deliver reliable products and services. Secure authentication, next-gen integration offers ease in handling traffic; SDK finance’ includes best-in-class personalization.
  4. Oracle Flexcube: Packed with features that modernizes a bank’s core system for today’s digital and agile demands, Oracle Flexcube places significant value on enhanced customer engagement, increased insight generation, and rapid integration approaches via open architecture. The USP of Oracle’s product is the flexibility banks get in choosing transformation models, and the associated support across deployment models.
  5. Infosys Finacle: Finacle helps both traditional and emerging financial organizations in their digital transformations, focussed on frictionless customer experiences. Relying on Finacle, banks across 100 countries service 1B customers and $1.3 B accounts. Bringing a proposition of – ‘truly digital’ – Finacle’s core banking software offers flexible product factories, extensive parametrization, product building and reusable business components – all of which accelerates innovation-led growth.

In 2022, what is the consensus on core banking software – final words

Most incumbent banks are limited with legacy platforms. Approaching inflection points puts distance between the leaders and laggards, each passing day. The post-pandemic altered customer behavior and accelerated digitalization trends, makes it necessary to act without delay. Once an assessment of the current core banking platform is done, banks should choose solutions and software that greatly increase their odds of success.

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Top 5 Digital Transformation Challenges In Wealth Management

Top 5 Digital Transformation Challenges In Wealth Management

When it comes to meeting challenges, there are two truths. First, effective problem solving involves digging at the roots instead of hacking at the leaves. Second, a problem can’t be solved until we ask the right questions. So along with digital transformation challenges, let’s look at the dynamics that creates them, and ask questions that help us solve them.

How large is the AWM industry?

One PwC release talks about how with $110 T in assets (2020), the asset and wealth management (AWM) industry can quite literally shape a better future for investors, shareholders, the economy and the society. As one considers the growth projection of 5.6% YoY, and the resultant size of $147.4 T (2025), the enormity of it all sinks in.

Moving from ‘What-if’ to ‘What-is’.

For years now, AWM industry could have used benefits that fell off the pandemic. It is an odd statement to make (after all, no one wants an event of such scale and destruction). In the context of AWM digital transformation, wealth management systems primarily functioned with back-office support and on legacy platforms. The digital presence was limited. The anachronistic practice worked on one-to-one, product-led advisement. Not anymore. Consumers today discover products digitally and decide differently.

Technology undoubtedly is at the heart of the modernization, but successful wealth management transformations need to be customer-centric powered by scalable next-gen platforms.

With that caution, let’s get down to the top 5 digital transformations in AWM

  1. Managing the wired investor. Slow adaptation to demographic shifts.

No longer is the lucrative AWM segment made up of 50-year-olds. The segment sees a growing number of HNW millennials and women. A recent world wealth report posits that HNW clients are likely to transfer $68 trillion within 25 years to their heirs. Firms and technologies, as the study highlights, are neither prepared to manage expectations of new-age beneficiaries nor have a plan to arrest 80% of heirs that are likely to change financial advisors. For meeting the unprecedented scenario, both advisor experience and machine-learning approaches are necessary.

Transformation Trigger

Do your solutions fuse optimal elements from science-based advice (asset allocations, mutual funds selections etc.) with human-based advice (more complex needs – tax & asset planning etc.)?

  1. Rise of emotional analytics. AI-based wealth management.

In a client-advisor relationship, human emotions guide and reveal the most important component: trust. Even so, the same validated human behaviour AI technologies deployed in making autonomous vehicles and remote medicine, are enabling financial advisors to bring up best interest by detecting (and managing) client behaviours.

Transformation Trigger

For your AWM organization, what is the appetite for AI-based targeting and personalization initiatives through emotional analytics solutions?

  1. Behind the curve on Big Data and Analytics. Creating Insightful Personas.

Gauging real-time market sentiments, hyper-personalizing services, and providing for omni-channel customers who have several bank accounts, call for advanced analytics. AWM firms using simple analytics (MIS reporting systems) for mining customer insights, and assessing product penetration, are woefully behind in the digital transformation race.

Transformation Trigger

When it comes to Data Analytics, how much of time are you making for tracking business performance, client acquisition, sales & retention, and ultimately, client advice?

  1. Underutilizing the power of API’s. Growing reach.

AWM companies that are in a fast catch-up mode are prioritising their business tactics – calibrating clients, clearing out legacy systems, and rationalizing portfolios.

Success of these repair initiatives will however depend on how deep they engage with the ecosystem and equip workforce with newer capabilities.

Eventually, their growth narrative will have to face up to two imperatives:

  1. Bring in agility through platform customizations and
  2. Enable remote access that covers wider geographies

These two essential value-plays are fulfilled through API’s (application programming interfaces).

Transformation Trigger

For introducing new functionalities, sharing analytics with client, or even, for reducing costs, how well-versed is your wealth management company with ‘everything-API’?

  1. Embracing Digital Transformation at the core. Holistic goals-based advice.

While AWM organizations make investment in Data, Analytics, AI and other technologies, most fail to embrace it as a culture. There are exceptions though. Like the Toronto-based TD bank group’s program, called WealthACT. Today investment advice is largely commoditized for mass market offerings. Also, post-pandemic, the investing environment has grown uncertain and complicated. To combat this, leading AWM firms invest substantially to train employees on goals-based advisory frameworks.

The digital transformation challenge, however, comes from a lack of access to right tools and a maturity-approach that asks for a complete embrace of ‘digital as a culture’.

Transformation Trigger

In your AWM company, does the digital transformation programs specifically include skills for understanding emerging tech, and building empathy for the ‘new-wired’ customer?

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