Report

ai in financial asset management market

AI in Financial Asset Management Market – Global Forecast up to 2025

pages Pages: 67
tables Tables: 28
charts Charts: 44
country Regions/Countries: 12
compaines Companies: 8
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[67 Report Pages ] In recent years, financial institutions are adopting the artificial intelligence (AI) technology for managing their financial assets and reducing operating cost, thereby increasing the revenue. Several fintech companies and banks are rapidly deploying voice assistants and chatbots to manage customer interactions and resolve issues (queries) with minimal human involvement. Machine learning, computer vision, and speech recognition technologies are in demand and major number of acquisitions in the recent years were associated with these technologies, and the same technologies will dominate the investment patterns in the coming years

Major areas where AI could be deployed in manging financial assets include fraud detection, personal financial management, and investment banking. With the implementation of financial asset management, the financial institutions can effectively manage their financial assets and meet expectations of the changing customer behavior by leveraging technologies, including AI, predictive analytics, and machine learning. This will assist organizations in automation and improves business processes, thus resulting in enhanced customer’s experience.

The global AI in financial asset management market is categorized based on the presence of diversified small and large vendors. Genpact, IBM, Infosys, and Synechron are among the key vendors increasing their global footprint in this space. However, various vendors such as IPsoft and Lexalytics are competing with them in the global market by providing solutions at a competitive price with the customized product offering. The market growth is fuelled by key vendors entering into strategic partnerships with suppliers and third-party vendors in the ecosystem to increase the global footprint and customer service capabilities.

Natural language processing (NLP) is the fastest growing technology in the global AI in financial asset management market owing to the increasing deployment of chatbots and virtual personal assistants in the banking sector. Additionally, increasing demand for sentiment analysis and management of huge volumes of contracts, will drive the NLP segment during the forecasted period.

Data analysis is having the largest market share in the application segment of the global AI in financial asset management market primarily due to availability of huge volumes of data being generated from multiple sources and need to analyse theses datasets for decision making. Investment banks are implementing AI in the areas such as investment decisions, alternative investment strategies, managing hedge funds and others.

According to Infoholic Research, the global AI in financial asset management market is expected to grow at a CAGR of 33.84% during the forecast period 2019–2025. The aim of this report is to define, describe, segment, and forecast the AI in financial asset management market on the basis of technology, application, and regions. In addition, the report helps the venture capitalists in understanding the companies better and make well-informed decisions. The report is primarily designed to provide the company’s executives with strategically substantial competitor information, data analysis, and insights about the market, development, and implementation for an effective marketing plan.

The global AI in financial asset management market is categorized based on three segments – technology, application, and regions.


  • Technology includes Predictive Analytics, Machine Learning, NLP, and Others

  • Application includes Conversational Platforms, Data Analysis, Risk & Compliance, Portfolio Optimization, Process Automation, and Others

  • Regions include Americas, Europe, APAC, and RoW (RoW includes Middle East and Africa; APAC includes East Asia, South Asia, South-East Asia, and Oceania)

  • The report comprises an analysis of vendors, which includes financial status, business units, key business priorities, SWOT, business strategies, and views.

  • The report covers the competitive landscape, which includes mergers & acquisitions, joint ventures & collaborations, and competitor comparison analysis.

  • In the vendors profile section, for the companies that are privately held, the financial information and revenue of segments will be limited.


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The key players offering AI in financial asset management across the globe include:

 


  • Genpact

  • IBM

  • Infosys

  • Synechron

  • Next IT

  • IPsoft

  • Lexalytics

  • Narrative Science


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1                Executive Summary

2                Industry Outlook

2.1            Industry Snapshot

2.1.1        Industry Overview

2.1.2        Industry Trends

3                Market Snapshot

3.1            Total Addressable Market

3.2            Segmented Addressable Market

3.2.1        PEST Analysis

3.2.2        Porter’s Five Force Analysis

3.3            Related Markets

3.4            Market Segmentation

3.5            Market Dynamics

3.5.1        Drivers

3.5.1.1    Adoption of intelligent systems in the data-driven financial sector

3.5.1.2    Rapid proliferation of new investment vehicles

3.5.1.3    Changing customer behavior and expectation

3.5.2        Restraints

3.5.2.1    Reluctance among financial institutions to deploy fully autonomous systems

3.5.2.2    Workforce inability to react to AI outcomes while managing financial assets

3.5.3        Opportunities

3.5.3.1    Implementation of AI for mergers & acquisitions

3.5.3.2    Adoption of cognitive systems in core banking operations

3.5.4        DRO – Impact Analysis

4                AI in Financial Asset Management Market, By Technology

4.1            Overview

4.2            Predictive Analytics

4.3            Machine Learning

4.4            NLP

4.5            Others

5                AI in Financial Asset Management Market, By Application

5.1            Overview

5.2            Conversational Platform

5.3            Data Analysis

5.4            Risk & Compliance

5.5            Portfolio Optimization

5.6            Process Automation

5.7            Others

6                AI in Financial Asset Management Market, By Geography

6.1            Overview

6.2            Americas

6.3            Europe

6.4            APAC

6.5            RoW

7                Competitive Landscape

7.1            Competitor Analysis

7.2            Product/Offerings Portfolio Analysis

7.3            SWOT Analysis

7.4            Market Developments

7.4.1        Mergers & Acquisitions

7.4.2        Expansions

7.4.3        Product Launches & Exhibitions

8                Vendors Profile

8.1            Genpact

8.1.1        Analyst Opinion

8.1.2        Business Analysis

8.1.2.1    Strategic snapshot

8.1.2.2    Business impact analysis

8.1.2.3    Operational snapshot

8.1.2.4    Product/service portfolio

8.2            IBM

8.2.1        Analyst Opinion

8.2.2        Business Analysis

8.2.2.1    Strategic snapshot

8.2.2.2    Business impact analysis

8.2.2.3    Operational snapshot

8.2.2.4    Product/service portfolio

8.3            Infosys

8.3.1        Analyst Opinion

8.3.2        Business Analysis

8.3.2.1    Strategic snapshot

8.3.2.2    Business impact analysis

8.3.2.3    Operational snapshot

8.3.2.4    Product/service portfolio

8.4            SYNECHRON

8.4.1        Analyst Opinion

8.4.2        Business Analysis

8.4.2.1    Strategic snapshot

8.4.2.2    Business impact analysis

8.4.2.3    Operational snapshot

8.4.2.4    Product/service portfolio

8.5            Next IT

8.5.1        Analyst Opinion

8.5.2        Business Analysis

8.5.2.1    Business impact analysis

8.5.2.2    Operational snapshot

8.5.2.3    Product/service portfolio

8.6            IPsoft

8.6.1        Analyst Opinion

8.6.2        Business Analysis

8.6.2.1    Strategic snapshot

8.6.2.2    Business impact analysis

8.6.2.3    Operational snapshot

8.6.2.4    Product/service portfolio

8.7            Lexalytics

8.7.1        Analyst Opinion

8.7.2        Business Analysis

8.7.2.1    Strategic snapshot

8.7.2.2    Business impact analysis

8.7.2.3    Operational snapshot

8.7.2.4    Product/service portfolio

8.8            Narrative Science

8.8.1        Analyst Opinion

8.8.2        Business Analysis

8.8.2.1    Strategic snapshot

8.8.2.2    Business impact analysis

8.8.2.3    Operational snapshot

8.8.2.4    Product/service portfolio

9                Annexure

9.1            Report Scope

9.2            Research Methodology

9.2.1        Data Collation & In-house Estimation

9.2.2        Market Triangulation

9.2.3        Forecasting

9.3            Study Declarations

9.4            Report Assumptions

9.5            Abbreviations

Research Framework

Infoholic Research works on a holistic 360° approach in order to deliver high quality, validated and reliable information in our market reports. The Market estimation and forecasting involves following steps:

  • Data Collation (Primary & Secondary)
  • In-house Estimation (Based on proprietary data bases and Models)
  • Market Triangulation
  • Forecasting
Methodology

Market related information is congregated from both primary and secondary sources.

Primary sources

Involved participants from all global stakeholders such as Solution providers, service providers, Industry associations, thought leaders etc. across levels such as CXOs, VPs and managers. Plus, our in-house industry experts having decades of industry experience contribute their consulting and advisory services.

Secondary sources

Include public sources such as regulatory frameworks, government IT spending, government demographic indicators, industry association statistics, and company publications along with paid sources such as Factiva, OneSource, Bloomberg among others.

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AI in Financial Asset Management Market – Global Forecast up to 2025
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