Global NLP in Finance Market Analysis Report 2023

The “Global NLP in Finance Market by Offering (Software, Services), Application (Customer Service & Support, Risk Management & Fraud Detection, Sentiment Analysis), Technology (Machine Learning, Deep Learning), Vertical, and Region – Forecast to 2028″ report has been added to ResearchAndMarkets.com’s offering.

The global NLP in finance market is projected to grow from USD 5.5 billion in 2023 to USD 18.8 billion by 2028 at a compound annual growth rate (CAGR) of 27.6%.

The market is anticipated to grow due to the increasing demand for automated and efficient financial services and rising need for accurate and real-time analysis of complex financial data.

By offering, managed services under services segment to register for fastest growing market rate during forecast period

The market for managed services in NLP in finance is expected to grow significantly in the coming years due to the increasing demand for NLP capabilities in the finance industry. The market is highly competitive, with several established players offering a wide range of NLP services to financial institutions of all sizes.

Some of the key players in this market include IBM, Amazon Web Services, Google, Microsoft, and SAS. These services allow financial institutions to focus on their core business while outsourcing NLP tasks to experts who have the necessary infrastructure, technology, and expertise to provide accurate and efficient NLP solutions.

By vertical, insurance segment to register fastest growing CAGR during forecast period

Insurance is a financial product that protects against unforeseen events or losses. NLP is increasingly used in the insurance industry to improve various processes, including underwriting, claims processing, customer service, and fraud detection.

One of the key areas where NLP is used in insurance is underwriting. Insurance companies use NLP to analyze large amounts of data from various sources, such as social media, credit scores, and medical records, to assess risk and determine premiums.

North America to account for the largest market size during the forecast period

The presence of a growing tech-savvy population, high internet penetration, and advances in AI has resulted in the growth of NLP solutions used in the finance sector. Most of the customers in North America have been leveraging NLP to improve their efficiency, reduce costs, and enhance the customer experience, ultimately leading to better business outcomes.

The rising popularity and higher reach of NLP further empower SMEs and startups in the region to harness NLP technology as a cost-effective and technologically advanced tool for building and promoting business, growing consumer base, and reaching out to a wider audience.

Premium Insights

Increasing Popularity of Chatbots Across Finance and Improving Performance of NLP Models to Drive Market Growth
Customer Service and Support Application Segment to Account for Highest Growth Rate
Software and Banking to be Largest Shareholders in North America in 2023
North America to Hold Largest Market Share in 2023

Market Dynamics

Drivers

Increasing Demand for Automated and Efficient Financial Services Worldwide
Rising Need for Accurate and Real-Time Analysis of Complex Financial Data
Emergence of AI and ML Models

Restraints

Lack of Standardization in NLP-based Financial Applications and Services
Difficulty in Managing Large Volumes of Unstructured Data
Complexity in Developing and Training Sophisticated NLP Models

Opportunities

Development of Customized NLP Solutions for Specific Financial Services and Use Cases
Integration of NLP with Blockchain and Big Data to Enhance Accuracy and Efficiency of Financial Operations
Growing Adoption of NLP-Powered Chatbots and Virtual Assistants

Challenges

High Implementation Costs Associated with NLP
Limited Availability of Skilled Professionals
Data Privacy Concerns Associated with Use of NLP

Ethics and Implications of NLP in Finance

Bias and Fairness
Privacy and Security
Intellectual Property
Accountability and Responsibility
Societal and Economic Impact

Ecosystem Analysis

NLP in Finance Technology Providers
NLP in Finance Cloud Platform Providers
NLP in Finance API and As-A-Service Providers
NLP in Finance Hardware Providers
NLP in Finance End-users
NLP in Finance Regulators

NLP in Finance Tools and Framework

Tensorflow
Pytorch
Keras
Nltk
Apache Opennlp
Spacy
Gensim
Allennlp
Flair
Stanford Corenlp

Case Study Analysis

Case Study 1: Natwest Improved Speed and Accuracy of Complaint-Handling Process Through IBM
Case Study 2: Ayasdi’s NLP Platform Helped J.P. Morgan Chase Ramp Up Risk Assessment Techniques
Case Study 3: Capital One Eliminated Inefficiencies in Customer Query Resolution Through NLP
Case Study 4: Blackrock Identified New Investment Avenues by Analyzing Large Volumes of Unstructured Data
Case Study 5: Yseop Assisted Td Ameritrade in Discovering New Customer Insights
Case Study 6: Allianz Witnessed Substantial Improvement in Insurance Claims Processing Through NLP
Case Study 7: UBS Trained Datasets Through NLP to Augment Risk Management Processes
Case Study 8: Citi Added Personalized Touch to Customer Recommendations Via NLP-based Query Analysis
Case Study 9: Barclays Scaled Its Trading and Investment Analysis Processes Via Ayasdi’s NLP Tool
Case Study 10: Goldman Sachs Augmented Its Financial R&D Prowess
Case Study 11: NLP Empowered Kabbage with Smarter Decision-Making for Loan Disbursal
Case Study 12: Chainalysis Deployed NLP for Fraud Prevention in Crypto Trading