Tech Trends 2021: A financial services perspective has been saved
Perspectives
Tech Trends 2021: A financial services perspective
A glimpse into the future of financial services
The technologies that enhance our organizations and our lives are more powerful—and more essential—than ever before. Forward-thinking organizations, including those in financial services, understand the technological forces that surround them and look for ways to harness them for the benefit of all stakeholders.
Explore content
- Strategy, engineered
- Core revival
- Supply unchained
- MLOps: Industrialized AI
- Machine data revolution: Feeding the machine
- Zero trust: Never trust, always verify
- Rebooting the digital workplace
- Bespoke for billions: Digital meets physical
- DEI: Tools for equity
- Get in touch
- Join the conversation
Below we provide a financial services-specific take on Deloitte’s Tech Trends 2021 report, spotlighting the accelerating technology trends most likely to cause disruption over the next 18 to 24 months.
From the rise of strategy and technology becoming inseparable, to the rapidly disappearing boundary between the physical and virtual worlds, the trends we explore could have profound implications for business, finance, and society in the months and years to come.
Readiness and relevance scale:
We looked at each trend and assigned a value from one (low) to five (high) based on the trend’s relevance to and readiness for financial services adoption.
- Readiness: How ready are financial services organizations to address this trend over the next 18 to 24 months?
- Relevance: How relevant will this trend be to financial services organizations over the next 18 to 24 months?
Strategy, engineered
As business and technology strategy become increasingly inseparable, technology choices bear a greater role in enabling—or potentially constraining—organizational strategy. Leading firms are delivering significant franchise value by creating data-driven and technology-enabled competitive advantages.
How can you embrace the trend toward a tech-enabled business strategy, optimized for agility?
Getting started:
● Assess current state: Measure your organization’s current leadership and operating models against leading practices to identify potential gaps.
● Bolster leadership: Bring together key leaders to workshop future scenarios, assess areas of agreement and disagreement, and articulate how your business needs to evolve to gain a competitive edge.
● Embrace new ways of working: Shift talent and funding mechanisms to support your transition and define one or several North Stars to drive and execute a top-down vision.
Trend in action:
● Differentiate core offerings: Data and tech can help enhance client experience, drive operational efficiency, and apply analytics to boost salesperson productivity.
● Expand and adapt: New tech can help extend mobile capabilities and enable expansion into naturally adjacent markets, allowing for new forms of financial advice in consumer banking, wealth, or asset management to flourish.
● Access new products and revenue streams: Tech-enabled strategy can help create new, sustainable revenue streams, such as licensing internally developed tech platforms to competitors or launching a new business.
Readiness: 3/5
Relevance: 5/5
Core revival
As the C-suite increasingly views technology modernization as an imperative to enable strategic change, pioneering IT leaders are embracing new approaches, technologies, and business cases to revitalize core assets.
How can you harness new technologies, techniques, and business cases to drive your modernization strategy?
Getting started:
● Reconsider legacy tools: Legacy technology works, but it will keep costing more to maintain and isn’t built to support the future pace of change.
● Revise processes: Modernizing technology can help you rethink outdated processes and operations.
● Use technology wisely: Consider the products and services you sell or support, and ensure those that introduce complexity are core to your business model.
Trend in action:
Several catalysts are driving reinvestment in core systems after many years of being funded as a “keep-the-lights-on” expense
● Fintech innovation: Next-generation, cloud-native core platforms have now reached the marketplace, creating simpler implementation efforts and lower-risk deployment options.
● End-of-life announcements: Starting in 2022, several prolific platforms in the financial services sector will no longer be eligible to receive support from product developers.
● Robotics-assisted renewal: Automated mining and code-scanning capabilities are enabling institutions to unlock years of buried code that can enable rapid rule and logic migration.
Readiness: 2/5
Relevance: 4/5
Case study: GM Financial uses PaaS to build stronger systems for customers
Instead of migrating to a third-party platform, GM Financial opted to modernize their legacy loan origination systems using cloud platform-as-a-service (PaaS). As a result, they gained a competitive advantage, gave their partners reliable tools to support their work, and created a stable, manageable production environment for IT to continue to innovate with minimal effort.
Supply unchained
Pioneering companies are using advanced digital technologies, virtualized data, and cobots to transform supply chain cost centers into customer-focused, value-driving networks.
How can you transform a traditional cost center into a value driver?
Getting started:
● Identify gaps in IT security: Review IT security in your technology, people, and end-to-end processes.
● Optimize systems and processes: Continuously mine data for operational insights.
● Assess third-party risk: Conduct a rigorous evaluation of data privacy, nonperformance, unethical conduct, and the loss of business continuity.
Trend in action:
● Replace disparate systems: Intuitive digital platforms with automated tools streamline end-to-end processes and provide a single digital/mobile-enabled customer solution while ensuring transparency and reducing risk.
● Consider customer privacy: Customer privacy-related expectations are being built into third-party and intermediary agreements and contracts.
● Use tech to enhance traditional systems: Insurance companies are using drones to improve data collection, analysis, and actionable insights, as well as reducing operational costs by making claims adjudication, processing, and customer experience more efficient.
Readiness: 3/5
Relevance: 3/5
MLOps: Industrialized AI
To shorten development life cycles and industrialize artificial intelligence (AI), we must give way to MLOps: applying the engineering discipline to automate machine learning (ML) model development, maintenance, and delivery.
How do you go about scaling model development and operations with a dose of engineering and operational discipline?
Getting started:
● Prioritize AI and ML: Highlight use cases based on technology stack, level of complexity, need for retraining, and potential business impact.
● Develop a road map: Determine how to build different MLOps capabilities and determine near- and short-term priorities.
● Start building: Create data science and data engineering pipelines for selected use cases required to support model development and deployment processes.
Trend in action:
● Build data resilience programs: Find new ways to support bank payments, foreign exchange (FX), and wires; automated data discovery; and anomaly detection engines.
● Problem solve: For example, Deloitte created a deal-level classification model, agent scorecard analysis, and an intervention framework to target at risk customers in Commercial Corporate.
● Deploy scalable technology: Find technology solutions for context extraction and ingestion of unstructured data forms.
● Migrate to supported platforms: For example, a leading US insurance carrier migrated from Teradata to Snowflake just to support MLOps.
Readiness: 4/5
Relevance: 4/5
Case study: Morgan Stanley | Scaling to thousands of models in financial services
Morgan Stanley was looking to scale hundreds of fraud detection and prevention, sales and marketing automation, and personalized wealth management models into the thousands. To do this, an in-house model risk management team was established, leveraging MLOps to increase the number of models in production, operationalize them more efficiently, and use AI to drive better business decisions.
Machine data revolution: Feeding the machine
Achieving the benefits and scale of AI and MLOps requires tuning data for native machine consumption, leading many organizations to rethink data management, capture, and organization.
How can your organization rethink its data management value chain for the age of machine learning?
Getting started:
● Modernize legacy data infrastructure: Financial services organizations will need to adapt to cloud-first, real-time integration and metadata-driven and preventative control frameworks.
● Embrace novelty: When it comes to discovering and connecting, it’s important to help data come alive using a modernized approach like AI and a knowledge graph–enabled data fabric.
● Deliver insights at the right time: In some cases, the “right time” is at the point of interaction, and in others, it’s long after the relevant event has occurred. Enabling architecture to support both these patterns is a must.
Trend in action:
● Design strategy and architecture: Outline use cases that are more digital, automated, and AI-enabled and address them using cloud-first, real-time integration and metadata-driven and preventative control frameworks.
● Focus on delivering value: Meet the demands of your customers and organization more effectively while balancing the buildout of the core capabilities defined in the architecture.
● Adopt a “fail fast, learn fast” approach: Build architecture incrementally to address these use case requirements, and learn the do’s and don’ts along the way.
Readiness: 3/5
Relevance: 5/5
Case Study: ABN AMRO | Banking on distributed data architecture
ABN AMRO’s legacy data management models were not designed to respond to constant read queries and real-time updates. So, rather than engineering workarounds to accommodate the data pulsing through its systems, the Dutch bank used a three-pronged approach to create a data replication model with multiple cloud vendors, allowing its data scientists to fix quality issues at the source and focus on turning data into value.
Zero trust: Never trust, always verify
A Zero Trust cybersecurity posture provides the opportunity to create more robust and resilient security, simplify security management, improve end-user experience, and enable modern IT practices.
How can you maximize security in the age of the porous perimeter?
Getting started:
● Avoid a big-bang approach: Organizations should take an iterative and incremental approach toward zero trust adoption, leveraging existing technologies and capabilities where possible.
● Start with low-risk targets: Minimize disruption by starting with low-risk targets before attempting to implement additional zero trust–enabled controls around your crown jewels.
● Prioritize business needs over technology: Adopt zero trust through relevant business drivers and areas of transformation rather than focusing on technology implementation and adoption.
● Expect a cultural shift: Organizations should assess and address the potential impact on end users, operational teams and processes, business stakeholders, and relevant third parties.
Trend in action:
Zero trust projects are typically tied to broader transformation initiatives to drive and enable business alignment. Trending use cases in financial services include the following:
● Digital transformation
● Cloud adoption and migration
● Mergers and acquisitions (M&A) integration
● Third-party risk management (TPRM)
● Secure remote access
● Technical and cyber resilience
● Network segmentation and microsegmentation
● Modernized identity management
Alignment with zero trust guiding principles enables organizations to deliver on these initiatives and be “secure from the start.”
Readiness: 3/5
Relevance: 5/5
Rebooting the digital workplace
The digital workplace represents a fundamental shift in the way work gets done. Organizations are embracing technology to optimize individual and team productivity, collaboration, and the employee experience at large.
How can you use data to drive new ways of working remotely and in the office?
Getting started:
● Keep people in mind: Pinpoint personas and use human-centered design to develop new requirements for the digital workplace and redesign the workforce experience.
● Understand the landscape: Learn the nuances of the digital workplace tech landscape to determine the gap between your current state and desired design.
● Focus on the work: Remember not to lose sight of your goals when creating new ways of working and leadership norms in the digital workplace.
Trend in action:
● Use human-centered design: Identify “cohorts” based on preferences and work to inform their digital workplace needs.
● Accelerate digital investment: Keep in mind the need to reprioritize work to enable key future-state capabilities.
● Adopt new ways of working: Focus more directly on rearchitecting work, capabilities, and decisions needed to enable the digital workplace.
Readiness: 2/5
Relevance: 5/5
Case study: Lloyd’s of London accelerates innovation with virtual underwriting room
After halting in-person trading due to COVID-19, Lloyd’s of London established a virtual underwriting room to connect brokers and underwriters on digital collaboration platforms, enabling them to schedule trading conversations with colleagues around the world and improving work-life balance in the process.
Case study: JLL | Human-centric technology critical to workplace redesign and workforce performance
In a recent survey conducted by commercial real estate services company JLL, nearly half of respondents said they desire offices with dedicated areas for socializing, learning, connecting to nature, and doing focused work. To meet those needs, businesses will need to reenvision the workplace as a social hub. JLL is exploring how AI, virtual and artificial reality, 3D modeling, and other technologies can enable the dispersed workforce to collaborate and innovate in the future.
My Take: Dan Torunian | Vice president, employee technology and experiences and data centers, PayPal
When COVID-19 moved PayPal’s 23,000+ employees from 90 onsite locations to working from home, the company was challenged to explore new ways to deliver the same capabilities across multiple collaboration platforms to optimize remote worker experiences across regions. By identifying cultural working norms, differences in IT infrastructure, and technology preferences that could affect employee work styles and processes, PayPal became better able to help its employees effectively meet their objectives; serve customers in more than 200 countries; and enjoy the flexibility of working from their bedroom office, coffee shop, or desk while using their preferred tools.
Bespoke for billions: Digital meets physical
Driven to embrace digital faster than ever, organizations are recognizing that the ultimate “human” experience strikes a balance between physical and digital.
How can you create more “human” experiences at scale?
Getting started:
● Think about customer outcomes: Develop a journey-based channel strategy based on the desired behavioral, economic, and emotional outcomes you’d like to achieve.
● Adopt an outcomes-based “test-and-learn” approach: Experiment with existing and emerging technologies to design and scale more personalized experiences that recognize moments of impact.
● Prioritize omnichannel approaches: First, focus on experience design, API and orchestration service prioritization, operating model design, and incentives.
Trend in action:
● Humanize call center experience: Natural language processing (NLP) sentiment analysis can help ensure the AI voice matches the subject of the call, determining likely customer mindset before an agent engages. For example, a customer calling with an insurance claim is likely to appreciate an empathetic voice.
● Stay true to your brand voice: As organizations implement conversational AI, marketing should ensure the AI voice selected for each channel matches the tone of their brand.
Readiness: 2/5
Relevance: 4/5
DEI: Tools for equity
Organizations have access to increasingly sophisticated tools to support their diversity, equity, and inclusion (DEI) initiatives across the talent life cycle and make decision-making processes more data-driven.
How can you elevate the technology leader’s role in propelling workforce imperatives?
Getting started:
● Identify areas that lack diversity: Technology leaders can help do so by reengineering the way data is collected, managed, analyzed, and reported.
● Review DEI workforce strategies: Evaluate partnerships, responsible data practices, and feedback mechanisms.
● Embrace technology: Use technology to support DEI outcomes across all aspects of the employee journey—from talent sourcing and selection to employee experience, compensation, retention, and development.
Trend in action:
● Achieve financial inclusion goals: Financial institutions are working with fintech companies to lower transaction and service costs, fees, and penalties to reach underserved consumers.
● Implement human capital management suites: The suites can offer cloud-based analytics and dashboards that can be customized to support DEI across the talent life cycle.
● Embed technology solutions: Technology leaders are building integrated solutions into their organizations’ technology stack and processes to drive DEI across the workplace.
Readiness: 2/5
Relevance: 4/5
Discover more about Tech Trends on Deloitte Insights
Explore content
- Strategy, engineered
- Core revival
- Supply unchained
- MLOps: Industrialized AI
- Machine data revolution: Feeding the machine
- Zero trust: Never trust, always verify
- Rebooting the digital workplace
- Bespoke for billions: Digital meets physical
- DEI: Tools for equity
- Get in touch
- Join the conversation
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