Top 5 Expectations in the Future of Finance in ‘23

The finance industry stands at a crossroads. Revolutionary technologies like artificial intelligence and blockchain combined with shifting consumer preferences are transforming the landscape. As we enter 2023, here are the 5 biggest expectations for the future of finance based on today‘s most influential trends:

  1. Aggressive pursuit of automation across financial processes
  2. Widespread adoption of AI for enhanced decision making
  3. Increased use of blockchain for transparency and efficiency
  4. Upskilling of workforces in technical capabilities
  5. Hyper-personalized customer experiences

In this comprehensive guide, we will explore what’s driving these changes and how they might impact banks, insurance companies and other financial services firms. Let’s examine each of these 5 key trends in detail.

Section 1: The Automation Revolution

Process automation using robotic process automation (RPA), artificial intelligence (AI) and advanced analytics will become pervasive across financial services. Cost reduction and efficiency gains remain top priorities for finance leaders, and intelligent automation delivers on both fronts.

According to ResearchAndMarkets, global spending on RPA software in banking reached $1.9 billion in 2021. This reflects astonishing 64% year-on-year growth, demonstrating how aggressively banks are adopting automation. The RPA market across industries is forecasted to exceed $13 billion by 2028.

Graph showing increasing RPA market size from 2022 to 2028

RPA market size forecast (ResearchAndMarkets)

A survey by Deloitte found that 73% of financial enterprises already have RPA initiatives underway. And 91% plan to increase their RPA budgets. The most commonly automated banking processes today include:

  • New account opening
  • Lending operations
  • Sanctions and AML monitoring
  • Financial reporting
  • Payments processing
  • Data reconciliation

But RPA is just the starting point. AI and machine learning will take automation to the next level. For example, JP Morgan developed an AI-based contract management system called COiN that reviews documents and extracts important data points. This system handles loan-related tasks that previously required over 360,000 hours of legal staff review per year.

According to a McKinsey analysis, over 60% of finance roles in middle- and back-office functions could possibly be automated by 2030 using maturing technologies like NLP, computer vision and prescriptive analytics. The greatest automation potential lies in areas like underwriting, claims management and compliance monitoring.

But front-office, client-facing roles are relatively safer with automation potential under 30% by 2030, per McKinsey‘s projections. This is because activities like financial advising require emotional intelligence and human judgment.

McKinsey graph showing automation potential across finance functions

Automation potential across finance (McKinsey)

Speaking at a conference, Deutsche Bank CEO Christian Sewing predicted that up to 50% of the bank‘s current employees could eventually be replaced by machines and technology.

Financial services firms are increasingly looking to "hyperautomation" strategies that combine RPA, AI, analytics, IoT, biometrics and other emerging technologies. This creates intelligent process automation on an immense scale.

For instance, China‘s online bank JD Finance used RPA and AI to automate over 2,000 processes in less than 2 years. The output? Improved efficiency for 70% of operational and management tasks, freeing employees to focus on value-added work.

So automation in its many flavors will undoubtedly reshape finance roles and skillsets. The workforce of the future will need to adapt to remain relevant.

Section 2: The Rise of AI

Artificial intelligence promises tremendous value across financial services, including:

  • Predictive analytics
  • Fraud detection
  • Investment decisions
  • Risk modeling
  • Personalized recommendations
  • Customer service

According to Allied Market Research, global expenditure on AI in banking is projected to grow from $9.9 billion in 2022 to $30.4 billion in 2027. Driving this growth is AI‘s proven ability to drive revenue, reduce costs and improve customer experience.

For instance, Persistent Systems helped a leading Asian bank use AI to reduce false positives in anti-money laundering monitoring by 30%. This improved efficiency and regulatory compliance with lower operational costs.

JP Morgan attributes over $700 million of incremental revenue to its AI-powered contract management system COiN. The natural language processing technology reviews legal documents and extracts key data points in seconds.

AI‘s predictive capabilities make it particularly beneficial. Using predictive models, Capital One analyzes customer transaction data to detect emerging trends. This helps inform new personalized product offerings.

Going forward, broader adoption of advanced language models like ChatGPT will expand AI’s utility for financial institutions. Generative AI can summarize reports, analyze sentiments from customer calls, generate content and more.

According to a survey by Opimas, 56% of financial organizations plan to deploy generative AI. Key applications include:

  • Automated report writing
  • Text analytics
  • Risk modeling
  • Competitive intelligence
  • Personalized marketing

But to be effective, AI needs vast amounts of quality, unbiased data for model training. This is challenging in regulated industries like finance. Synthetic data generation is emerging as a solution.

For example, Persona synthesizes fake identities with realistic attributes to use as training data for AI credit risk models. This boosts model accuracy without compromising on privacy regulations.

In the future, we can expect AI to become integral across financial institutions as companies invest in developing in-house capabilities. Expertise in MLOps, data science and analytics will be highly coveted.

Section 3: Advancing with Blockchain

Blockchain is transforming backend processes in finance with its cryptographic security, decentralized structure and shared ledger capabilities.

A Cambridge Centre for Alternative Finance survey found that 65% of financial services firms are actively exploring blockchain-based solutions. Major applications include:

Cross-border payments – Facilitating real-time international money transfers and reducing transaction fees through stablecoins. In 2022, JP Morgan used its JPM Coin blockchain network to make payments between the U.S. and Germany in minutes.

Trade finance – Digitizing trade documents like purchase orders and automating tracking of shipment status via smart contracts. IBM and Maersk‘s blockchain platform TradeLens has onboarded over 300 organizations.

Digital identity – Securely storing customer KYC data on blockchain for seamless account openings across institutions. Australia‘s ANZ bank offers this capability.

Regulatory compliance – The immutable ledger provides transparency over transactions and asset flows by recording all activity in detail. This simplifies compliance.

Claims processing – Smart contracts can trigger automatic claim payouts based on IoT data or external events like flight delays. Startups like Etherisc are working on such solutions.

According to Allied Market Research, the global blockchain in banking and finance market size reached $1.7 billion in 2021. It is projected to grow at a meteoric 68% CAGR from 2022 to 2031 as adoption increases.

Graph showing increasing blockchain in banking and finance market size

Blockchain in banking and finance market size (Allied Market Research)

Banks are also launching their own blockchain-based digital currencies. In 2021, JPMorgan issued the first cryptocurrency representing fiat money value from a major U.S. bank.

And over 20 central banks worldwide are piloting CBDCs to modernize national payment infrastructures. According to a survey of central banks by the Bank for International Settlements, 86% are actively researching CBDCs.

Blockchain is transforming existing finance processes. But it also enables new operating models such as decentralized finance (DeFi – providing banking services on public blockchains) and embedded finance (integrating financial services into non-finance platforms).

As the technology matures, companies that strategically leverage blockchain for transparency, automation and disintermediation will gain a competitive advantage.

Section 4: Reskilling the Workforce

As financial institutions embrace automation and emerging technologies like AI and blockchain, their workforce skill requirements are changing rapidly.

According to the World Economic Forum, by 2025 finance roles will comprise:

  • 55% hybrid human-machine tasks
  • 36% purely human skills like creativity, leadership and negotiation
  • Only 9% fully automated jobs

This means employees must be continuously trained in digital capabilities like:

  • Cloud computing
  • Data science and analytics
  • Process mining
  • RPA development
  • AI/ML modeling
  • Blockchain architecture

"By 2030, we‘ll need a radically different mix of skills," predicts Deutsche Bank CEO Christian Sewing. Technical skills will be in demand, while jobs lost to automation will require redeployment.

Reskilling employees is critical for a smooth transition into this tech-powered future. Banks like Deutsche and HSBC are rolling out skilling programs including:

  • Virtual classrooms
  • Microlearning through mobile apps
  • Lab simulations
  • Online certifications
  • Gamification
  • Job shadowing

Deutsche Bank‘s AI Academy has provided over 8000 employees with hands-on training in data, AI and cloud skills so far.

But progress may be too slow. According to a Gartner survey, only 17% of heads of HR say they have plans to reskill current staff to fill emerging roles. A talent deficit looms large.

Hiring externally is also difficult. McKinsey estimates that through 2030, there could be demand for 323,000 US financial services employees with AI skills but supply may fall short by over 100,000.

Proactive reskilling is the most sustainable solution. Firms able to equip employees with specialized technical expertise and a learn-how-to-learn mindset will thrive.

Section 5: The Personalization Imperative

Today‘s consumers expect hyper-personalized experiences from their financial providers. They are willing to share personal data in exchange for tailored recommendations, predictive insights and contextual cross-sells.

According to Salesforce research, 67% of consumers want financial firms to use their data to identify products that best meet their needs. And 72% are open to primary banks accessing external account data to build a complete financial profile.

Armed with permission to customers‘ financial data, banks have an opportunity to evolve into holistic advisors and partners. By combining internal and external data, then applying analytics and AI, they can deliver:

Personalized nudges – Increase retirement savings contributions when income data shows a raise.

Early warnings – Notify customers of potential overdrafts based on real-time expenditure tracking.

Contextual advice – Suggest umbrella insurance when weather APIs show a storm approaching.

Next best actions – Prompt mortgage refinancing when market interest rates drop.

BMO Harris built an AI assistant called BMO Insights that analyzes customers‘ transactions to offer personalized tips on spending, saving and managing cash flow.

But convenience remains table stakes in today‘s mobile era. Banking customers demand seamless omnichannel access across web, mobile, ATMs, call centers and branches.

Silicon Valley Bank unified 37 systems onto one platform to provide consistent personalized experiences across channels. They saw a 15% increase in multi-product households, illustrating the revenue potential of personalization.

Without individualized attention at scale, companies risk attrition. A 2022 Bain survey found that loyalty scores declined across financial services categories like retail banking, insurance and investing.

Hyper-personalization must be the way forward. As Alex Jimenez, Chief Strategy Officer at Extractable says: "Personalization at a mass level – that is true digital transformation."

The finance landscape in 2024 and beyond will be defined by automation, AI, blockchain, upskilling and customer-centricity.

As emerging technologies and rising expectations disrupt age-old practices, financial institutions must boldly reinvent legacy processes before they are rendered obsolete.

Banks and insurers who embrace these key trends will successfully evolve for the digital-first era. Those who lag risk losing relevance and customers.

The future awaits those who start preparing today. What is your organization doing to transform for the finance industry of tomorrow?

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