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AI in BFSI Market to hit US$ 140 billion by 2028, Says Global Market Insights Inc.

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The AI in BFSI Market is expected to surpass USD 140 billion by 2028, as reported in a research study by Global Market Insights Inc. The market growth can be attributed to the emergence of fintech technology and innovations in banking processes along with digitization.

The growing demand to analyze, report, and collect a large volume of data and gain meaningful insights to support banking processes will support market growth. The increasing adoption of advanced technologies including big data, blockchain, cloud computing, and biometrics generates extensive data. AI-based solutions are integrated with machine learning algorithms to help banks in collecting and analyzing data. It provides an in-depth analysis of the customer data and helps banks to make decisions, enabling operational efficiency and gaining higher ROI.

Request for a sample of this research report @ https://www.gminsights.com/request-sample/detail/2605

The data analytics & visualization solution segment is expected to grow at a CAGR of over 30% from 2022 to 2028. The demand for data analytics across financial enterprises has increased exponentially, which is attributed to the continuously growing digital data and increasing inclination toward the customer-centric business model. Data analytics & visualization solutions help BFSI enterprises in analyzing a large volume of structure & unstructured data and provide in-depth analysis. They also help organizations to identify customer needs and provide personalized services.

The computer vision technology segment is anticipated to grow significantly over the forecast timeline. Insurance and wealth management companies are using automated processing with computer vision technology to analyze digital information. Enterprises leverage computer vision to automate the analysis of digital information such as images, content, etc. This technology is also used in processes including underwriting and automated data extraction.

The customer service segment is projected to hold a market share of above 40% in the global AI in BFSI market by 2028. Growing competition in the BFSI sector and the need to acquire a large customer base are driving enterprises to focus on improved customer relationships. AI-based chatbots are being deployed extensively in the financial services sector to improve service delivery, customer query handling, and assisting customers in banking transactions.

The adoption of AI-based solutions for wealth management is growing significantly and is expected to register lucrative gains by 2028. The use of AI-backed tools helps wealth managers to advise customers based on analyzed historical data and present analysis. It also provides AI-powered recommendations and insights specifically tailored to customer requirements. For instance, in November 2021, Verint, Inc. announced AI-driven Real-time Agent Assist capabilities. These capabilities included sentiment analysis and new work assist function, which offered brands a robust tool to help employees and raise customer satisfaction and loyalty.

North America is anticipated to hold a substantial revenue share in the AI in BFSI market by 2028. The COVID-19 outbreak has severely impacted the regional BFSI sector. In response, the companies operating in the market are developing innovative solutions to help banking & financial institutions to drive their business. For instance, in April 2020, Temenos, a leading banking software company, launched explainable AI models for banks. This enabled credit unions & banks to provide rapid loans to SMEs and retail customers.

The companies operating in the market are focusing on the development of advanced AI-based solutions to help banking institutions in data-driven applications. Technology companies are leveraging AI and ML capabilities to improve the data collection processes, which is expected to drive the demand for AI-based solutions from financial institutions to modernize data collection process and improve customer experience. For instance, in February 2022, Intel introduced an upgraded version of the OpenVINO toolkit. This update added Natural Language Processing (NLP) support, improved device portability on hardware and improved inferencing performance. This tool helps users to develop solutions to handle tasks including automatic speech recognition, NLP, and emulation of human vision.

Request for customization of this research report at https://www.gminsights.com/roc/2605

Some major findings of the AI in BFSI market report include:

  • The increasing adoption of cloud-based services and demand for virtual assistance across BFSI enterprises will support technology development in the market.
  • North America is expected to hold a major market share in AI solutions due to hefty investments in AI technology by companies such as AWS and Google.
  • The major players operating in the AI in BFSI market are Amazon Web Services, Google LLC, Intel Corporation, Microsoft Corporation, and Oracle Corporation, among others.
  • The companies operating in the market are focusing on the development of innovative AI-based solutions such as explainable AI (XAI) models and platforms.

Partial chapters of report table of contents (TOC):

Chapter 2 Executive Summary

2.1  AI in BFSI industry 360º synopsis, 2018 – 2028

2.1.1  Business trends

2.1.2  Regional trends

2.1.3  Component trends

2.1.4  Technology trends

2.1.5  Application trends

2.1.6  End-use trends

Chapter 3 AI in BFSI Industry Insights

3.1  Introduction

3.2  Impact of COVID-19 outbreak

3.3  Evolution of AI in BFSI technology

3.4  AI in BFSI industry ecosystem analysis

3.5  Investment portfolio

3.6  Patent analysis

3.7  Regulatory landscape

3.8  Use cases

3.9  Industry impact forces

3.9.1  Growth drivers

3.9.1.1  Exponentially growing digital data

3.9.1.2  Rising investment in AI

3.9.1.3  Increasing partnership between financial institutes and fintech companies

3.9.1.4  Growing need to provide enhanced customer experience

3.9.2  Industry pitfalls & challenges

3.9.2.1  Data safety & security

3.9.2.2  Black box effect with AI tools

3.10  Growth potential analysis

3.11  Porter’s analysis

3.12  PESTEL analysis

Fintech

How to identify authenticity in crypto influencer channels

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Modern brands stake on influencer marketing, with 76% of users making a purchase after seeing a product on social media.The cryptocurrency industry is no exception to this trend. However, promoting crypto products through influencer marketing can be particularly challenging. Crypto influencers pose a significant risk to a brand’s reputation and ROI due to rampant scams. Approximately 80% of channels provide fake statistics, including followers counts and engagement metrics. Additionally, this niche is characterized by high CPMs, which can increase the risk of financial loss for brands.

In this article Nadia Bubennnikova, Head of agency Famesters, will explore the most important things to look for in crypto channels to find the perfect match for influencer marketing collaborations.

 

  1. Comments 

There are several levels related to this point.

 

LEVEL 1

Analyze approximately 10 of the channel’s latest videos, looking through the comments to ensure they are not purchased from dubious sources. For example, such comments as “Yes sir, great video!”; “Thanks!”; “Love you man!”; “Quality content”, and others most certainly are bot-generated and should be avoided.

Just to compare: 

LEVEL 2

Don’t rush to conclude that you’ve discovered the perfect crypto channel just because you’ve come across some logical comments that align with the video’s topic. This may seem controversial, but it’s important to dive deeper. When you encounter a channel with logical comments, ensure that they are unique and not duplicated under the description box. Some creators are smarter than just buying comments from the first link that Google shows you when you search “buy YouTube comments”. They generate topics, provide multiple examples, or upload lists of examples, all produced by AI. You can either manually review the comments or use a script to parse all the YouTube comments into an Excel file. Then, add a formula to highlight any duplicates.

LEVEL 3

It is also a must to check the names of the profiles that leave the comments: most of the bot-generated comments are easy to track: they will all have the usernames made of random symbols and numbers, random first and last name combinations, “Habibi”, etc. No profile pictures on all comments is also a red flag.

 

LEVEL 4

Another important factor to consider when assessing comment authenticity is the posting date. If all the comments were posted on the same day, it’s likely that the traffic was purchased.

 

2. Average views number per video

This is indeed one of the key metrics to consider when selecting an influencer for collaboration, regardless of the product type. What specific factors should we focus on?

First & foremost: the views dynamics on the channel. The most desirable type of YouTube channel in terms of views is one that maintains stable viewership across all of its videos. This stability serves as proof of an active and loyal audience genuinely interested in the creator’s content, unlike channels where views vary significantly from one video to another.

Many unauthentic crypto channels not only buy YouTube comments but also invest in increasing video views to create the impression of stability. So, what exactly should we look at in terms of views? Firstly, calculate the average number of views based on the ten latest videos. Then, compare this figure to the views of the most recent videos posted within the past week. If you notice that these new videos have nearly the same number of views as those posted a month or two ago, it’s a clear red flag. Typically, a YouTube channel experiences lower views on new videos, with the number increasing organically each day as the audience engages with the content. If you see a video posted just three days ago already garnering 30k views, matching the total views of older videos, it’s a sign of fraudulent traffic purchased to create the illusion of view stability.

 

3. Influencer’s channel statistics

The primary statistics of interest are region and demographic split, and sometimes the device types of the viewers.

LEVEL 1

When reviewing the shared statistics, the first step is to request a video screencast instead of a simple screenshot. This is because it takes more time to organically edit a video than a screenshot, making it harder to manipulate the statistics. If the creator refuses, step two (if only screenshots are provided) is to download them and check the file’s properties on your computer. Look for details such as whether it was created with Adobe Photoshop or the color profile, typically Adobe RGB, to determine if the screenshot has been edited.

LEVEL 2

After confirming the authenticity of the stats screenshot, it’s crucial to analyze the data. For instance, if you’re examining a channel conducted in Spanish with all videos filmed in the same language, it would raise concerns to find a significant audience from countries like India or Turkey. This discrepancy, where the audience doesn’t align with regions known for speaking the language, is a red flag.

If we’re considering an English-language crypto channel, it typically suggests an international audience, as English’s global use for quality educational content on niche topics like crypto. However, certain considerations apply. For instance, if an English-speaking channel shows a significant percentage of Polish viewers (15% to 30%) without any mention of the Polish language, it could indicate fake followers and views. However, if the channel’s creator is Polish, occasionally posts videos in Polish alongside English, and receives Polish comments, it’s important not to rush to conclusions.

Example of statistics

 

Wrapping up

These are the main factors to consider when selecting an influencer to promote your crypto product. Once you’ve launched the campaign, there are also some markers to show which creators did bring the authentic traffic and which used some tools to create the illusion of an active and engaged audience. While this may seem obvious, it’s still worth mentioning. After the video is posted, allow 5-7 days for it to accumulate a basic number of views, then check performance metrics such as views, clicks, click-through rate (CTR), signups, and conversion rate (CR) from clicks to signups.

If you overlooked some red flags when selecting crypto channels for your launch, you might find the following outcomes: channels with high views numbers and high CTRs, demonstrating the real interest of the audience, yet with remarkably low conversion rates. In the worst-case scenario, you might witness thousands of clicks resulting in zero to just a few signups. While this might suggest technical issues in other industries, in crypto campaigns it indicates that the creator engaged in the campaign not only bought fake views and comments but also link clicks. And this happens more often than you may realize.

Summing up, choosing the right crypto creator to promote your product is indeed a tricky job that requires a lot of resources to be put into the search process. 

Author Nadia Bubennikova, Head of agency  at Famesters

Author

Nadia Bubennikova, Head of agency at Famesters

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Fintech

Central banks and the FinTech sector unite to change global payments space

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The BIS, along with seven leading central banks and a cohort of private financial firms, has embarked on an ambitious venture known as Project Agorá.

Named after the Greek word for “marketplace,” this initiative stands at the forefront of exploring the potential of tokenisation to significantly enhance the operational efficiency of the monetary system worldwide.

Central to this pioneering project are the Bank of France (on behalf of the Eurosystem), the Bank of Japan, the Bank of Korea, the Bank of Mexico, the Swiss National Bank, the Bank of England, and the Federal Reserve Bank of New York. These institutions have joined forces under the banner of Project Agorá, in partnership with an extensive assembly of private financial entities convened by the Institute of International Finance (IIF).

At the heart of Project Agorá is the pursuit of integrating tokenised commercial bank deposits with tokenised wholesale central bank money within a unified, public-private programmable financial platform. By harnessing the advanced capabilities of smart contracts and programmability, the project aspires to unlock new transactional possibilities that were previously infeasible or impractical, thereby fostering novel opportunities that could benefit businesses and consumers alike.

The collaborative effort seeks to address and surmount a variety of structural inefficiencies that currently plague cross-border payments. These challenges include disparate legal, regulatory, and technical standards; varying operating hours and time zones; and the heightened complexity associated with conducting financial integrity checks (such as anti-money laundering and customer verification procedures), which are often redundantly executed across multiple stages of a single transaction due to the involvement of several intermediaries.

As a beacon of experimental and exploratory projects, the BIS Innovation Hub is committed to delivering public goods to the global central banking community through initiatives like Project Agorá. In line with this mission, the BIS will soon issue a call for expressions of interest from private financial institutions eager to contribute to this ground-breaking project. The IIF will facilitate the involvement of private sector participants, extending an invitation to regulated financial institutions representing each of the seven aforementioned currencies to partake in this transformative endeavour.

Source: fintech.globa

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Fintech

TD Bank inks multi-year strategic partnership with Google Cloud

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TD Bank has inked a multi-year deal with Google Cloud as it looks to streamline the development and deployment of new products and services.

The deal will see the Canadian banking group integrate the vendor’s cloud services into a wider portion of its technology solutions portfolio, a move which TD expects will enable it “to respond quickly to changing customer expectations by rolling out new features, updates, or entirely new financial products at an accelerated pace”.

This marks an expansion of the already established relationship between TD Bank and Google Cloud after the group previously adopted the vendor’s Google Kubernetes Engine (GKE) for TD Securities Automated Trading (TDSAT), the Chicago-based subsidiary of its investment banking unit, TD Securities.

TDSAT uses GKE for process automation and quantitative modelling across fixed income markets, resulting in the development of a “data-driven research platform” capable of processing large research workloads in trading.

Dan Bosman, SVP and CIO of TD Securities, claims the infrastructure has so far supported TDSAT with “compute-intensive quantitative analysis” while expanding the subsidiary’s “trading volumes and portfolio size”.

TD’s new partnership with Google Cloud will see the group attempt to replicate the same level of success across its entire portfolio.

Source: fintechfutures.com

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