The State Of AI Adoption In Financial Services

IDC predicts worldwide revenues for artificial intelligence (AI) software, hardware and services will reach $156.5B in 2020, increasing 12.3% over 2019.
The global AI fintech market is predicted to reach $22.6B in 2025, achieving a Compound Annual Growth Rate (CAGR) of 23.37% between 2020 and 2025 according to Mordor Intelligence.
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.
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.
54% of Financial Services organizations with 5,000+ employees have adopted AI, according to the latest Economist Intelligence Unit adoption study.

Strengthening customer relationships by providing exciting new services that protect everyone’s health while saving valuable time is proving to be the financial services’ greatest challenge. 60% of banks have closed or shortened opening hours of branches while fast-tracking new digital features, including automated account creation (34%), remote identification & verification (23%) and contactless payments (18%) according to Deloitte Digital’s Digital Banking Maturity 2020 Report.

Fast-tracking contactless, digital support across all channels generates terabytes of data a day that is essential for training supervised machine learning algorithms. Unsupervised machine learning algorithms rely on terabytes of data to discover previously unknown patterns in financial services data. AI is emerging as a new engine of growth by providing useful insights and intelligence in anxious, uncertain times.

Where AI Is Gaining Adoption In Financial Services

Financial Services firms are increasing their adoption AI and machine learning to capitalize on the data from new digitally driven channels. An Economist Intelligence Unit (EIU) research report found that 86% of Financial Services executives plan on increasing their AI-related investments through 2025. The EIU study, The Road Ahead: Artificial Intelligence and the Future of Financial Services, analyzes the sentiments of 200 business executives and C-suite leaders at investment banks, retail banks and insurance companies in North America, Europe and Asia-Pacific.

Key insights from the study highlighting the state of AI adoption across Financial Services includes the following:

Investment banking firms are the leading adopters of AI and machine learning technologies in Financial Services, closely followed by Retail. Investment banking operations rely on machine learning to fine-tune algorithms and prediction models to quantify and reduce risk. Retailers rely on predictive analytics to find new insights that can help retain customers and transition them from brick-and-mortar to digital channels.

37% of Financial Services firms globally adopt AI to reduce operational costs, followed by greater predictive analytics to improve decisions and scale up employee capacity to handle volume-based tasks. I’ve worked on business cases for AI projects that combine cost reduction, revenue gains and greater time efficiencies based on automation. North America leads all other regions in using AI for enhanced customer personalized service and customer satisfaction. The Economist research team found that 36% of heavy adopters also saw more efficient product and marketing services as a significant benefit, a view shared by just 23% of light adopters.

33% of North American Financial Services firms predict AI will change how they innovate, leading all other regions by a wide margin. North American firms are also the most optimistic regarding AI’s ability to enable them to release new products and services (31%). Financial Services executives from APAC and North America see the greatest opportunity to enter new markets (at 30% and 27%, respectively). According to the Economist research team, this reflects the higher rates of economic growth in both regions overall compared with the rest of the world and the level of AI investment from individual firms to support business growth.

Customer and stakeholder satisfaction is the most important key metric for measuring an AI strategy’s success in Financial Services today. AI projects in pilot and production this year are based on improving revenue potential by removing cost and time roadblocks. Launching new digital channels and getting the customer experience right the first time bring greater focus to customer and stakeholder satisfaction this year.

The high costs of AI technology are holding Financial Services firms back from increasing adoption in more areas of their organizations today. Cost constraints are slowing down AI adoption more than any other factor today. Insufficient infrastructure and data quality are the second and third -mentioned reasons for not adopting AI more broadly across an organization. The Economist’s research team found that 86% of Financial Service’s executives plan to increase AI-related investment into technology over the next five years, with the strongest views expressed in APAC (90%) and North America (89%). Investment in AI technologies could help resolve issues of legacy systems that have proved, along with systemic upgrades, a costly constraint financial services firms have had to deal with for decades.

Published at Sat, 31 Oct 2020 23:15:00 +0000

Artificial Intelligence in Energy Market Competitive Intelligence Analysis 2020-2028

Beathan Report has published the Global report on The Artificial Intelligence in Energy market, which is made up of advice about each of the important parameters of this market like consumption and the manufacturing patterns coupled with all the earnings patterns for the forecast period. Concerning creation aspect, the report provides complete detailed analysis about the manufacturing processes combined with the gross financials accumulated by the top most manufacturers working within this business. The primary facet of this Artificial Intelligence in Energy market that’s covered in the report helps the clients and the associations to better understand the business profile concerning drivers, restraints, challenges, and opportunities pertaining and affecting the marketplace dynamics.

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key players in this market include:

ABB

General Electric

IBM

Siemens

Grid4C

…

According to the Artificial Intelligence in Energy report, the

COVID-19 has majorly affected the Overall worldwide companies and it’ll take a enormous time for the company recovery. Vast majority of the business sectors have realigned their company strategies, priorities, and have amended their economic planning in order to remain in the company and keep their standing on the global platform. The thorough evaluation of this Artificial Intelligence in Energy market will enable the new market entrants to obtain reliable market approaches and strategy strong action plans for the prediction period.

by Type, the market is primarily split into

Machine Learning

Natural Language Processing

Others

by Application, this report covers the following segments

Load Research & Forecasting

Transmission & Distribution

Global Artificial Intelligence in Energy market: regional analysis, the major regions covered in the report are:

North America

United States

Canada

Europe

Germany

France

U.K.

Italy

Russia

Nordic

Rest of Europe

Asia-Pacific

China

Japan

South Korea

Southeast Asia

India

Australia

Rest of Asia-Pacific

Latin America

Mexico

Brazil

Middle East & Africa

Turkey

Saudi Arabia

UAE

Rest of Middle East & Africa

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Major highlights of this Artificial Intelligence in Energy market report:

* COVID-19 effect on the earnings Streams of the Artificial Intelligence in Energy market players.

* Statistics of the overall sales quantity And general market revenue.

* Industry trends breakdowns.

* Estimated expansion rate of this Artificial Intelligence in Energy Market.

* Pros and cons of the direct and

* In-depth information about the important Distributors, traders, and dealers.

Table of Contents : Artificial Intelligence in Energy Market

Part 01: Executive Summary

Part 02: Scope Of The Report

Part 03: Research Methodology

Part 04: Market Landscape

Part 05: Pipeline Analysis

Part 06: Market Sizing

Part 07: Five Forces Analysis

Part 08: Market Segmentation

Part 09: Customer Landscape

Part 10: Regional Landscape

Part 11: Decision Framework

Part 12: Drivers And Challenges

Part 13: Market Trends

Part 14: Vendor Landscape

Part 15: Vendor Analysis

Part 16: Appendix

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Published at Sat, 31 Oct 2020 22:07:30 +0000