- 70% of all financial services firms are using machine learning to predict cash flow events, fine-tune credit scores and detect fraud according to a recent survey by Deloitte Insights.
- 84% of enterprises believe AI has the potential to create and sustain a competitive advantage, while just 23% integrated AI into core processes, products or services.
- AI and machine learning are enabling fintech startups to outmaneuver their larger Financial Services competitors, attracting new customers who become loyal based on services traditional banks don’t offer.
- Aligning the production of AI with the consumption of AI increases the probability of success according to a recent MIT Sloan Management Review and Boston Consulting Group study.
AI and machine learning are reordering the Financial Services landscape, navigating an entire industry back to its customers. Fintech is forecast to achieve a compound annual growth rate (CAGR) of 25% through 2022, reaching a market value of $309B. The broader financial services market expected to reach $26.5T by 2022, achieving a 6% CAGR. AI and machine learning are the catalysts that every organization in Financial Services is either adopting or evaluating to break down silos, automate processes and remove barriers between themselves and their customers. In short, AI and machine learning deliver valuable new data and insights about customers and their needs that traditional Financial Services firms could not see before. The following graphic from the World Retail Banking Report, 2020 by Capgemini and Efma, reflects how traditional banks and financial services firms are not capitalizing on the data richness they have available to them. AI and machine learning are enabling startups and fast-moving cloud-based enterprise software companies including FinancialForce to capitalize on this gap.
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Financial Services Needs A Customer-Driven Digital Transformation
In recent discussions with CIOs and senior management team members at Financial Services firms, a few of which are former students of mine, the topic of how AI and machine learning is revolutionizing the financial services landscape came up. Concerned about how quick Fintech startups are infringing on their current services, a few of the CIOs are starting their innovation hubs internally. The most valuable takeaway from the innovation hubs so far: existing systems architectures can’t deliver a 360-degree view of customers and provide real-time responses across all services today. “We’re looking at how we can use AI and machine learning to integrate across data and system silos that were designed decades ago for much simpler business models,” one CIO said recently. “AI and machine learning are what we’re relying on to slice across all silos and provide real-time, drill-down financial reporting for our enterprise clients,” another said.
With decades of data and millions of dollars invested in legacy systems, Financial Services firms are relying on their enterprise software vendors to integrate AI and machine learning into the applications they already use. That’s proving to be the quickest and most trusted on-ramp to adopting AI and machine learning across the industry today. Deloitte Insights’ recent survey of AI and machine learning adoption in Financial Services found firms cluster in three performance categories of Starters, Followers and Frontrunners. Frontrunners lead all others based on their ability to embed AI in strategic plans and clearly define an organization-wide implementation plan. They’re also combining revenue and customer opportunities as part of their AI strategies not just cost reduction. What’s fascinating regarding Frontrunner’s adoption of AI is how six in ten looks to enterprise software vendors to provide integrated AI/cognitive features as part of their ongoing upgrades. The bottom line is that Frontrunners look for any speed and time-to-market advantage they can get, saving their most valuable resources and time to excel at mastering open-source AI/cognitive development tools (65%).
Improving Financial Analytics With AI
FinancialForce’s integration of Salesforce Einstein into its core product strategy reflects how Financial Services firms can find their way to greater competitive strength with AI and machine learning. Interested in seeing how the Einstein integration is working out and if it’s paying off for customers, I talked with Five9′s Blake Nelson, Senior Director of Operations. Blake explained how Five9s adopted FinancialForce to improve its Professional Services Automation (PSA) accounting, finance & reporting while reducing the hours spent by all five service divisions on non-billable activities that drain margin. Five9s built their business case on the time savings, tighter integration and ability to tighten up leaking inventory – all very challenging elements of a service business to quantify using an ERP system. When I asked him about the learning curve to use FinancialForce after abandoning a previous application that couldn’t keep up with their fast-growing business, Blake said FinancialForce’s Salesforce integration makes using their system transparent. “Our Professional Services teams spend hours every day in Salesforce and know the app quite well and given how tight the integration is with FinancialForce, it was a very quick learning curve – we see adoption exceed our initial goals.” FinancialForce’s ongoing efforts to bring AI and machine learning into their application is delivering results to customers and reflects the broader trend of driving adoption across services businesses by baking AI and machine learning right into the app.
FinancialForce’s Salesforce integration strategy is noteworthy in its depth and extent of support for AI-based reporting and financial analysis capitalizing on Einstein’s core strengths. The acid test of how well AI is integrated into any financial reporting and analytics application is how quickly and iteratively financial projects and recommendations can be created. Having transitioned to Salesforce Lightning and continuing to build on its design-in work with Einstein that started in 2019, FinancialForce’s latest release includes Revenue Trend Predictions Dashboard, PSA Billing Forecasting and a Subscription Bookings Dashboard. The following is an example of a predictions dashboard that can adapt to queries and provide content in real-time thanks to the integration of Lightning and Einstein.
AI and machine learning are the technologies of choice in Financial Services for silo-traversing workflows that are much-needed to deliver more value to customers. Fintech startups are capitalizing on the gap between what legacy systems can provide and what customers want. Salesforce’s open platform and advanced development environment are helping to bridge the gap by enabling software companies including FinancialForce to bring greater innovation into Financial Services. By making Einstein available as an embedded AI service, Salesforce also is achieving greater AI adoption across enterprises through its extensive partner network. Fintech startups design their cloud architectures with agility and time-to-market in mind, all focused on providing an excellent customer experience. It will be interesting to watch Salesforce’s partners attempt the same using their platform and DevOps tools.