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. 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%)....
Gender pay equity has become a big point of contention at many companies. Not only have politicians and other public figures spoken out against the gender pay gap, but there has also been a rising tide of high profile lawsuits targeting major employers, most notably in the U.S., with all the bad publicity and financial liability they entail.
In response, many firms have hired external pay consultants and law firms to identify whether they may have a problem with the pay gap from either an HR or legal perspective and to offer possible remedies. But in our view, the most common approaches for identifying a pay gap and resolving it are full of pitfalls for the unwary. Thatâs because itâs a tall order: you have to calculate the gap the right way and figure out how to fix it without ballooning your wage bill, all while truly helping underpaid women, maintaining your incentive structure, and avoiding the creation of new legal liabilities.
We have extensively researched the most common ways companies try to fix a pay gap â and how these fail or cause other problems â and weâve worked with several companies in different countries to solve their pay equity issues. Weâve found that closing a gender gap without regard to cost effectiveness can be prohibitively expensive; however, only focusing on cost (as many managers do) creates more problems than it solves....
The COVID-19 pandemic and recent social and political unrest have created a profound sense of urgency for companies to actively work to tackle racial injustice and inequality. In response, the Forum's Platform for Shaping the Future of the New Economy and Society has established a high-level community of Chief Diversity and Inclusion Officers. The community will develop a vision, strategies and tools to proactively embed equity into the post-pandemic recovery and shape long-term inclusive change in our economies and societies....
The coronavirus outbreak has been followed by a massive decline in economic activity in many countries, often blamed on the lockdowns aimed at stopping the spread of the disease and limiting the deaths it causes.IPSOS-Mori found a majority of Britons said they were still uncomfortable about a wide range of activities, including going to a bar or restaurant, large public gatherings, using public toilets or public transport.This may be one reason why some venues that could reopen are choosing not to - although the restrictions they would have to operate under make it harder to earn a profit even if the customers were to return....
