Category Archives: Artificial Intelligence

FinTech refers to any idea or innovation that improves or optimizes the way individuals or companies conduct financial activities. Early FinTech concentrated on developing add-on products to complement existing financial services.

This combination of finance and technology has spawned a slew of valuable goods and services that redefine financial services and make them more accessible to the general public. Some of these products and services include insurance aggregators, mobile wallets, AI investment management advisers, peer-to-peer (P2P) lending and crowdfunding tools, and platforms for trading financial assets. The cutting-edge solutions that contributed to such technologies include Blockchain, Deep Learning, and Artificial Intelligence (AI). FinTech allows financial services organizations to collect massive amounts of consumer data, determine usage patterns, and even replace human participation with automated algorithms.

The distinction between banks and FinTech is becoming increasingly hazy. It is crucial to understand that banks and FinTech are not necessarily mutually exclusive. In fact, many well-known banks have evolved from being mere prepaid card providers that link to applications. They have won the right to full-fledged banking licenses after demonstrating to the world that it is feasible to combine sophisticated technology with trustworthy financial services. FinTech can emerge from one of three sources: (1) a stand-alone company develops technologically advanced goods to address unique market concerns; (2) a company develops a full-fledged body to become a complete bank; or (3) a conventional bank incorporates technological advancement by acquiring a smaller FinTech to modernize its service.

SIGNIFICANCE OF ARTIFICIAL INTELLIGENCE IN FINTECHArtificial intelligence (AI) is gradually gaining a foothold in practically every business in the twenty-first century. FinTech is used mostly to improve and automate different financial operations. With the advent of knowledge engineering, financial institutions employ AI-based models in conjunction with their FinTech apps to maximize operations and revenue.

Some of the major significant uses of AI in FinTech are:

Large-scale wealth and finance management: Traditionally, the wealth management sector has catered to high-net-worth individuals. AI solutions are assisting to considerably expand this industry by allowing it to scale its ability to supply to a much larger segment of the population. In addition to specific financial advice, the AI may analyze spending habits to ensure that customers have adequate emergency money and provide continually updated net worth predictions for improved retirement planning.

Enhanced security: Many FinTech firms and conventional financial institutions are already using AI-based solutions for various fraud monitoring and prevention applications, but there is always room for improvement as fraudsters escalate their attacks.

Contract management: Contracts are an integral aspect of the financial business, as they are in many other sectors. Keeping track of all contracts, whether between institutions and clients or between enterprises, requires a significant amount of effort. AI can assist in speeding up this process by combining optical character recognition (OCR), machine learning (ML), and natural language processing (NLP).

Improved customer services: Through intelligent software bots, AI has been able to fill a need in this field. These bots suggest personalized products and services that better meet customers needs and demands. Financial institutions that employ chatbots have ample motivation to keep using and enhancing them, with worldwide savings from chatbot use anticipated to exceed $7 billion by 2023.

RISK FACTORS ASSOCIATED WITH ARTIFICIAL INTELLIGENCE IN FINTECHWhile the applications listed above demonstrate how technology is revolutionizing the financial industry, the deployment of AI is not without hazards. The primary factors that need to be understood are:

Embedded bias: The increasing use of AI in the financial sector, which is heavily regulated and where public confidence is critical, has sparked debate over the potential of inherent bias. Embedded bias is defined as computer systems that routinely and unjustly discriminate against some persons or groups of individuals in favor of others. Customer classification algorithms applied in AI/ML might lead to prejudice in the banking industry through price or service quality differentials. Biases in AI/ML judgments are frequently caused by biased training data derived from existing biased processes and data sets, which teach automation models to be prejudicial.

Explainability and complexity: The term explainability refers to the concept that AI models and their outputs can be expounded to humans at an acceptable level. The explainability of AI results is critical, especially when utilized in the financial industry. Because they are not easily explainable by the user, AI is sometimes referred to as a black box. This trait may make detecting the appropriateness of AI conclusions difficult, exposing businesses to vulnerabilities such as skewed data, inappropriate modeling methodologies, or wrong decision making, thus undermining faith in their robustness.

Privacy factors associated with data: AI raises new and distinct privacy concerns. Big data privacy problems are well recognized and even precede the mainstreaming of automation. Tools have been created to aid in the preservation of data anonymity and data subjects privacy. Legal data policy frameworks are being implemented across the world to address these problems. However, the resilience of AI models in limiting data leakage from the training data set poses additional privacy problems.

Cognizant of the abounding risks, enterprises should leverage on the agile nature of technology and adapt to new work methods. This would reshape the organizations ways of doing things through the use of intelligent software bots, minimize manpower costs by utilizing AI and robotic process automation, and most importantly, enhance security, employee engagement, and client satisfaction. As the financial sectors use of AI and ML continues to surge, it is becoming a must to have professionals who are very much capable of optimizing the usage of advances in processing power, data storage capacity, big data, and modeling.

The views or opinions expressed in this article are solely those of the author and do not necessarily represent those of Isla Lipana & Co. The content is for general information purposes only, and should not be used as a substitute for specific advice.

Jan Brian P. Despi is a senior associate at the DTS Department of Isla Lipana & Co., the Philippine member firm of the PwC network.

jan.brian.despi@pwc.com

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Artificial Intelligence: FinTech's innovation driver - BusinessWorld Online

Sanjay Gupta, Global Head of Product, at Mitek shares his 2022 predictionson the rise of touchless technologies, the impacts of digital banking, and the increasing use of voice and behavioral biometrics to verify identification.

Touchless technology will gain adoption in new industries

Technology designed to reduce friction in consumer behavior and improve the overall user experience will continue to see adoption, and in more sectors than years past. Weve seen airlines begin to rollout touchless airport technologies to speed passengers through airports. In 2022, well see more airlines use touchless technologies, as well as new industries like concert/sports arenas, public transportation, stores, and more. In addition to decreasing wait times in lines, biometrics-based authentication also prevents ticket fraud, an issue that's been on the rise in recent years as more consumers are buying digital event tickets.

During the height of COVID fears, consumers were no longer coming into big branches to manage their finances, and competition from smaller more digitally focused banking groups began to steal customers away. As a result,banks poured an influx of funds into their digital strategies to add more value for digitally-minded consumers, and in seeing the success of those efforts last year, will continue to push for moremobile-first, customer-focused technologies in 2022. These digital services will focus heavily on minimizing friction to make apps faster and easier to access. Combining a focus on digital technologies with consumers longstanding trust in the banking industry will also provide opportunities for banks to explore newer and sometimes riskier financial services, such as decentralized finance, where consumer interest is growing.

Voice and behavioral biometrics are the future of authentication

The use of AI, including biometrics, to verify identification and support secure online transactions will continue to expand in 2022.According to new researchfrom airports that have implemented biometrics systems in 2021, many consumers support these common types of identity verification, such as fingerprint matching. In the next year, we will hear more about behavioral biometrics and voice matching as these new methods enable people to conduct business and transactions online more securely.

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Artificial Intelligence authentication in 2022 - App Developer Magazine

Imagine a day in the future when you wake up to an alarm buzz of a smart assistant. Well, you may wonder is that not something available already in the present day? Thats right, but what if the smart assistant also transforms itself into a smart fitness trainer that can interact and guide on a personalised workout routine depending on your wellness goals, prevailing health conditions, and recovery needed based on previous nights sleep pattern?

Yes, in future, those smart assistants could become ones true wellness companion to help individuals keep up with their habits and maintain healthy lifestyle be it Mindfulness, Exercise, Diet, and Sleep (MEDS).

That day in the future is not too far away.

The pandemic has driven an unprecedented digital transformation and shifted the gears to accelerate on the adoption of at-home fitness and wellness technologies.

From smart mats and smart mirrors to kettlebells and cycling bikes, everything is connected to understand the users lifestyle better to create a more holistic and personalised fitness journey and deliver an immersive virtual workout experience straight to individuals fitness / living room.

Based on the evidence and various case studies, a healthy lifestyle with daily MEDS discipline has been found to hold the potential to reverse the lifestyle diseases and eventually replace the prescribed meds.

While it is true that world is slowly coming out of the pandemic and people have started cautiously stepping into the gyms, the pandemic fundamentally changed the way we live, play, work, and even workout.

Fitness revolution actually began a few years before the pandemic struck the world. The primary reason was the alarming rise in the heart disease due to inactive lifestyle and mental stress, which according to the WHO report has been the leading cause of death (70 percent) and besides the early onset age down to mid 30s was really concerning.

Around the same time, Yoga became prime movement ever since the UN declared International Day of Yoga on June 21 in 2015, and now, with 300 million practitioners globally. Yoga, a mindful practice of physical postures (asanas) in tandem with the right breathing pattern, when performed under the expert guidance, could reverse many lifestyle diseases such as diabetes, hypertension, GERD, chronic neck, and back pain, and in some cases doctors prescribed it as lifestyle medicine for faster rehabilitation of cancer patients.

As per Google Search, Yoga was reportedly the most widely searched topic during the pandemic, and naturally it has now become part of doctors prescription for preventive healthcare, resulting in the surge in demand for online yoga with content everywhere.

Last five years also observed technology evolution at a speed like never before. Artificial intelligence taking a place in our daily lives is a real thing. Specifically, healthcare industry has adopted AI and same is the case with its adjacent vertical of at-home fitness and wellness.

However, multiple challenges come in the way of integrating the ancient best practices into ones daily lifestyle for a stress-free life --lack of access to good wellness coaches, packed schedules, missing personalised attention in yoga classes, lack of consistent motivation, overwhelming online content causing injury than good, and lastly, no tangible ways to track ones progress.

Flexibility is a vital factor that has been associated with improved performance and reduced sports related injuries.

By adopting a mind-body lifestyle approach, yoga - that teaches mindfulness along with physical activity - is well suited for stress and symptom management. It is also safe to say that yoga along with modern medicines has been an effective intervention in the treatment of chronic non-communicable diseases

According to areport by Research N Reports, the value of the global fitness technology market is estimated to grow from $17.9 billion in 2019 to $62.1 billion by 2025.

Fast-paced lives and hectic lifestyles in the contemporary times have made it difficult for us to make time to go to the gym. Not everyone can find the motivation to travel long distances to work-out and this is where AI comes in the picture.

AI provides users with a step-by-step workout session that they can enjoy from the comfort of their home, finding balance between work and yoga. An AI-based assistant guides people to accurately perform asanas to enhance flexibility and balance.

Now, anyone can practice Yoga at home without travelling and worrying about additional expenses, anytime, with the help of voice assistant that gives flawless instructions to improve the poses of the practitioner.

A few visionary and innovative startups have already scripted this future with their smart and connected fitness tech equipment in the market and paving the way for this tech to evolve and build a healthy generation and happy community in future.

This technological advancement must be unconventional for Yogis but these concepts are gearing up to reach larger audiences. Virtual Yoga assistance is being adopted by people in large numbers lately, making obvious that Tech Yoga is here to stay.

(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)

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How artificial intelligence will power the future of fitness and well-being - YourStory

PARIS & DAEGU, South Korea, January 05, 2022--(BUSINESS WIRE)--Iktos, a company specialized in artificial intelligence (AI) for novel drug design and Astrogen, a clinical and research-oriented biotech company focused in developing innovative new drugs for treatment of intractable neurological diseases today announced that the companies have entered into a research collaboration agreement aimed at discovery of innovative small molecule pre-clinical drug candidates for Parkinson's disease.

Under the terms of the agreement, Iktos will apply its proprietary active learning based deep docking and de novo structure-based generative modelling technologies to design and optimize novel compounds and expedite the identification of pre-clinical drug candidates targeting an undisclosed target for the treatment of Parkinsons disease. Astrogen will contribute to in-vitro/in-vivo efficacy screening of lead compounds/pre-clinical drug candidates and will lead the entire development process from pre-clinical stage. The companies will share responsibility for generating lead compounds and pursuing the optimal development path for selecting pre-clinical drug candidates.

"We are thrilled to collaborate with Astrogen, a leading biotech company based in S. Korea focused on developing innovative drugs for neurological diseases. We are proud and excited to announce our first collaboration deal in S. Korea bio-pharma sector commented Yann Gaston-Math, President and CEO of Iktos. "Our objective is to expedite drug discovery and achieve time and cost efficiencies for our global collaborators by using Iktoss proprietary AI platform and know-how. We are confident that together we will be able to identify promising novel chemical matter for the treatment of intractable neurological diseases. Our strategy has always been to tackle challenging problems alongside our collaborators where we can demonstrate value generation for new and on-going drug discovery projects."

Story continues

"We are very pleased to collaborate with Iktos, one of the leading AI companies in drug design and discovery. Iktos has successfully utilized their proprietary AI platform in multiple real world drug discovery projects as demonstrated by several collaborations established to date with leading global pharmaceutical companies. We are looking forward to this collaboration, as we believe that there is good chance to build up a mutually beneficial business model, by combining the strengths of biotech companies specialized in novel target identification and AI companies with their proprietary drug designing platform technology" commented JoonBeom Park, the director of Business Development at Astrogen.

About Iktos

Incorporated in October 2016, Iktos is a start-up company specializing in the development of artificial intelligence solutions applied to chemical research, more specifically medicinal chemistry and new drug design. Iktos is developing a proprietary and innovative solution based on deep learning generative models, which enables, using existing data, the design of molecules that are optimized in silico to meet all the success criteria of a small molecule discovery project. The use of Iktos technology enables major productivity gains in upstream pharmaceutical R&D. Iktos offers its technology both as professional services and as a SaaS software platform, Makya. Iktos is also developing Spaya, a synthesis planning software based upon Iktoss proprietary AI technology for retrosynthesis.

More information on: http://www.iktos.ai/

About Astrogen

Founded in 2017, Astrogen is a S.Korea based clinical and research-oriented biotech company developing treatment of intractable neurological diseases. The company is specialized in discovering new therapeutic targets, conducting efficacy tests of compounds, and planning/administration of clinical strategies. The lead candidate in the pipeline is AST-001, under phase 2 clinical development in S.Korea for Autism Spectrum Disorder (ASD). The company aims to become a leading a biopharmaceutical company in neurodegenerative and intractable neurological diseases by utilizing innovative business models.

More information on: http://www.astrogen.co.kr

View source version on businesswire.com: https://www.businesswire.com/news/home/20220105005498/en/

Contacts

Yann Gaston-Math (CEO), +33 6 30 07 99 26, contact@iktos.com

Somin He, Manager of Business Development, +82-70-5038-5190, somin@astrogen.co.kr

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Iktos and Astrogen Announce a Research Collaboration to Use Artificial Intelligence Platform for Drug Design against a Novel Parkinsons Disease Target...

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Artificial intelligence is restoring lost works by Klimt, Picasso and Rembrandt, but not everyone is happy about it - Bowling Green Daily News

The Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD program) was created to combat harmful biases in how artificial intelligence and machine learning is used.

KERA's Justin Martin talked with UNTHSC's Dr. Jamboor Vishwanatha, about what this means for North Texas.

Interview Highlights:

On the AIM-AHEAD program:

AIM-AHEAD is a consortium to promote artificial intelligence and machine learning to achieve health equity and also diversify the research workforce that is involved in the AI (artificial intelligence) and ML (machine learning) work. So it basically attacks two different issues.

One is the lack of diversity in the data that is currently used in the AI/ML field. And secondly, who is actually doing the work.

So this is actually a very, very significant program, and I think you mentioned about $100 million. It is one large investment that NIH has made in terms of diversity efforts.

On how artificial intelligence and machine learning affects health care:

Artificial intelligence and machine learning are in every walk of life. Basically, when you wear a smartwatch, you're pretty much collecting data. And then there are a lot of decisions that are made.

Artificial intelligence is used in the hospitals, in the clinics. It is being used in all walks of life. What is going to happen is some of the clinical decisions will all be based on machine learning, on artificial intelligence.

So it is quite important at this point for us to make sure that people are not left behind, that their data is used in developing the algorithms in making sure that any outcome is representative of all the population.

On how AI/ML lacks diversity:

There are two issues. One is the gender. It turns out that most of the people who are working in this area are predominantly male. I mean, so you don't really have gender equity.

Secondly, racial ethnic groups are not highly represented in the workforce that is currently doing artificial intelligence machine learning. And therefore, this is really critical that we involve all of the groups, all of the racial ethnic groups in the future workforce.

Dr. Jamboor Vishwanatha is regents professor and vice president of diversity and international programs at the University of North Texas Science Center in Fort Worth.

Interview highlights were lightly edited for clarity.

Got a tip? Email Justin Martin atJmartin@kera.org. You can follow Justin on Twitter @MisterJMart.

KERA News is made possible through the generosity of our members. If you find this reporting valuable, considermaking a tax-deductible gifttoday.

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$100 million awarded to UNT's Health Science Center to diversify field of AI - KERA News

The global market for artificial intelligence (AI) in computer vision is projected to grow at a compound annual growth rate (CAGR) of 39.4% during the projected period from 2022 to 2030, reaching US $ 20.76 billion in 2030. The global market for artificial intelligence (AI) computer vision in 2020 is US $ 9.16 billion.

Computer vision systems determine meaningful information from visual inputs such as digital images and videos, and take actions and recommendations based on that information. Computer vision is very similar to human vision, but there are some advantages to human vision. Through lifelong experience, human vision distinguishes objects, detects movements, and images. You can learn to determine if is correct, etc. Computer vision can work as well.

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Factors that influence market growth

Market driving force-Expanding the use of computer vision systems in automotive applications , The spread of emotional AI, high quality inspection and automation are driving the global market.

Market threats increasing interest in safety and security is the main cause of slowing global market growth.

Market Growth -Many automakers and IT giants are developing autonomous vehicles, which is driving the growth of the global artificial intelligence (AI) market for computer vision.

Market Opportunities Artificial Intelligence Government initiative to drive the development of (AI) related technologies provides an opportunity for the entire computer vision artificial intelligence (AI) market.

COVID-19 Impact Analysis

COVID-19 is a global and community Many industrial sectors and companies struggled to secure resources during the pandemic (COVID-19). As a result of the pandemic, artificial intelligence This is because the demand for (AI) technology is increasing and many high-tech companies are developing solutions to prevent, control and mitigate viruses. As a result, the computer vision artificial intelligence (AI) market is COVID-19. Achieved positive growth during.

Further Report Highlights

In the Type segment, the hardware segment dominates the global market for artificial intelligence (AI) in computer vision in 2020. In the

regional segment, the Asia-Pacific region will dominate the global market in 2020 . It is the result of increased investment by Chinese companies to expand the scope of computer vision technology.

North America is expected to see significant market growth. Government efforts to promote the introduction of computer vision in the region , have contributed to this growth.

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List of major companies in the global market profiling of artificial intelligence (AI) in computer vision NVIDIA Corporation Intel Corporation Microsoft Corporation IBM Corporation Qualcomm Amazon Web Services, Incorporated Google, LLC Meta Platforms, Incorporated Xilinx, Incorporated BASLER AG Other Prominent Players

Segmental Analysis

The global market for artificial intelligence (AI) in computer vision focuses on components, features, applications, end-uses, and regions.

Component-based segmentation hardware Processor (CPU) Central processing unit (GPU) Graphics processing unit (ASIC) Integrated circuit for specific applications (FPGA) Field Programmable Gate Array memory storage software

Function-based segmentation training interference

Application-based segmentation Industrial Non-industrial

End-use based segmentation Automotive related Consumer electronics Healthcare Agriculture Transportation / Logistics retail

Security and surveillance Manufacturing industry others

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By region,

North America America Canada Mexico

Europewestern Europe England Germany France Italy Spain Other Western European countries

Eastern Europe Poland Russia Other Eastern European countries

Asia-Pacific China India Japan Australia / New Zealand Association of Southeast Asian Nations Other Asia Pacific regions

Middle East / Africa (MEA)United Arab Emirates (UAE) Saudi Arabia South Africa Other Middle East / Africa regions

South America Brazil Argentina Other South American regions

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The Global Market for Artificial Intelligence (AI) in Computer Vision is projected to grow at a compound annual growth rate (CAGR) of 39.4% during the...

In the first of his four (stunning) Reith lectures on living with artificial intelligence, Prof Stuart Russell, of the University of California at Berkeley, began with an excerpt from a paper written by Alan Turing in 1950. Its title was Computing Machinery and Intelligence and in it Turing introduced many of the core ideas of what became the academic discipline of artificial intelligence (AI), including the sensation du jour of our own time, so-called machine learning.

From this amazing text, Russell pulled one dramatic quote: Once the machine thinking method had started, it would not take long to outstrip our feeble powers. At some stage therefore we should have to expect the machines to take control. This thought was more forcefully articulated by IJ Good, one of Turings colleagues at Bletchley Park: The first ultra-intelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control.

Russell was an inspired choice to lecture on AI, because he is simultaneously a world leader in the field (co-author, with Peter Norvig, of its canonical textbook, Artificial Intelligence: A Modern Approach, for example) and someone who believes that the current approach to building intelligent machines is profoundly dangerous. This is because he regards the fields prevailing concept of intelligence the extent that actions can be expected to achieve given objectives as fatally flawed.

AI researchers build machines, give them certain specific objectives and judge them to be more or less intelligent by their success in achieving those objectives. This is probably OK in the laboratory. But, says Russell, when we start moving out of the lab and into the real world, we find that we are unable to specify these objectives completely and correctly. In fact, defining the other objectives of self-driving cars, such as how to balance speed, passenger safety, sheep safety, legality, comfort, politeness, has turned out to be extraordinarily difficult.

Thats putting it politely, but it doesnt seem to bother the giant tech corporations that are driving the development of increasingly capable, remorseless, single-minded machines and their ubiquitous installation at critical points in human society.

This is the dystopian nightmare that Russell fears if his discipline continues on its current path and succeeds in creating super-intelligent machines. Its the scenario implicit in the philosopher Nick Bostroms paperclip apocalypse thought-experiment and entertainingly simulated in the Universal Paperclips computer game. It is also, of course, heartily derided as implausible and alarmist by both the tech industry and AI researchers. One expert in the field famously joked that he worried about super-intelligent machines in the same way that he fretted about overpopulation on Mars.

But for anyone who thinks that living in a world dominated by super-intelligent machines is a not in my lifetime prospect, heres a salutary thought: we already live in such a world! The AIs in question are called corporations. They are definitely super-intelligent, in that the collective IQ of the humans they employ dwarfs that of ordinary people and, indeed, often of governments. They have immense wealth and resources. Their lifespans greatly exceed that of mere humans. And they exist to achieve one overriding objective: to increase and thereby maximise shareholder value. In order to achieve that they will relentlessly do whatever it takes, regardless of ethical considerations, collateral damage to society, democracy or the planet.

One such super-intelligent machine is called Facebook. And here to illustrate that last point is an unambiguous statement of its overriding objective written by one of its most senior executives, Andrew Bosworth, on 18 June 2016: We connect people. Period. Thats why all the work we do in growth is justified. All the questionable contact importing practices. All the subtle language that helps people stay searchable by friends. All of the work we have to do to bring more communication in. The work we will likely have to do in China some day. All of it.

As William Gibson famously observed, the futures already here its just not evenly distributed.

Pick a sideThere Is no Them is an entertaining online rant by Antonio Garca Martnez against the othering of west coast tech billionaires by US east coast elites.

Vote of confidence?Can Big Tech Serve Democracy? is a terrific review essay in the Boston Review by Henry Farrell and Glen Weyl about technology and the fate of democracy.

Following the rulesWhat Parking Tickets Teach Us About Corruption is a lovely post by Tim Harford on his blog.

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Worried about super-intelligent machines? They are already here - The Guardian

AI

Theres nothing magical about artificial intelligence, its simply code designed by fallible humans using fallible data. The magic comes from the humans working with or seeing the benefits of AI. So the questions are: are we expecting too much from AI? Too what extent should companies and their executives rely on the output delivered by AI?

This was the subject of debate at a panel hosted at AI Summit in New York, held in early December, focusing on risks in the emerging role of AI in the financial services sector, but the discussion had wide-ranging implications across all industries. (I had the opportunity to co-chair the conference, and moderate the panel.)

We think AI is telling us something, but its not, cautioned Rod Butters, chief technology officer for Aible. Its just a bunch of code. It doesnt know. This is the fantasy we all fall into. Somehow we think that model has embodies something. The reality is that an AI is just a statistical engine, and in a lot of cases, its a bad statistical engine.

With AI these days, the biggest systemic risk in the notion that artificial intelligence is artificial, said Rik Willard, founder and managing director of Agentic Group, and member of the advisory board of the World Ethical Data Foundation. Its all done by humans; its all manifested by humans. When we look at risk versus returns, its only as good as the financial institutions, and the regulatory frameworks around those institutions. Are we supporting the same human and economic algorithms that we set up before technology, or are we working to make those better and more inclusive?

In addition, AI is still a relatively immature technology, said Drew Scarano, vice president of global financial services at AntWorks. Ten years ago we werent even talking about AI, but today, its a multi-billion dollar industry, he said. said Scarano. We might be too reliant on this technology, forgetting about the humans in the loop and how they play an integral part in complementing artificial intelligence in order to get desired results.

Another challenge is AI systems tend to get built in relative isolation. AI is just code, and the people building these systems may have limited perspectives on its value to the business, Butters cautioned. When we tell data scientists go out and create a model, were asking them to be a mind reader and a fortune teller, he said. Those are two bad job sets, it doesnt work. The data scientist is trying to do the right thing, creating a responsible and solid model, but based on what? Ultimately when they build a model, unless theyve got this combination to create transparency, create expandability, actually communicate that across to the business constituency both at a strategic and tactical, who is in charge? Just creating a great model does not necessarily solve all problems.

In the process of building data models, data scientists need to understand the objectives of the enterprise, taking into account the human implications, Scarano said. You can have engineer build a great bridge. So if its not going over what its intended to do, its just a great bridge, right? Im afraid that people in business, especially financial services. will just keep relying too much on technology. We need a holistic approach, in coexistence with humans.

Look beyond the technology and statistics of AI, and focus on what ultimately serves the customer, Scarano urged. Its about how we complement humans with artificial intelligence to drive business, and also drive customer reality, customer success and customer satisfaction at the end of the day.

The path to AI in service of business objectives relies on the establishment of consistent frameworks that guide its development, panelists agreed. I was raised in a fail-fast environment, said Willard. You build code, you test, and fix what's broken. You fix it on the fly. You build it, it kind of works, you let it loose, then you refine it over time based on input to the feedback loop. However, with AI, the issue is that we put it in a position of judgment. Like in the criminal justice system, where it does a lot of harm before you get it right. In the banking system its loan, no loan; score, no score; or credit, no credit. How do we build testing frameworks and sandboxes that have the accuracy thats necessary to be launched at scale, while doing less harm along the way?

AI is being used for many purposes across the financial services industry, but the risk is in de-humanizing the interpersonal qualities that helped build the industry. Today we can use AI for anything from approving a credit card to approving a mortgage to approving any kind of lending vehicle, said Scarano. But without human intervention to be able to understand there's more to a human than a credit score, there's more to a person than getting approved or denied for a mortgage.

Customer experience is the foundation of financial services, and this needs to be front and center of all AI initiatives. There needs to feedback loops in AI-driven systems that incorporate human input. As we implement AI-based solutions, we need to ensure that the end users, the customers, who are consuming the product are also happy with that investment and solution as well, said Robert Magno, solutions architect with Run:AI. It makes a lot of sense to have robots moving packages around, automated in a warehouse. But from a customer service standpoint, if a person interacting with a chatbot is getting frustrated, there needs to be a feedback loop to ensure solutions you're implementing are resonating with your customers, and they're enjoying the experience as much as you're enjoying creating the experience.

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Artificial Intelligence May Be Just Code, But Its Our Code - Forbes

Dublin, Dec. 29, 2021 (GLOBE NEWSWIRE) -- The "AI and Advance Machine Learning in BFSI Market By Component, Deployment Model, Enterprise Size and Application: Global Opportunity Analysis and Industry Forecast, 2021-2030" report has been added to ResearchAndMarkets.com's offering.

Artificial intelligence (AI) in finance is transforming the BFSI industry, as AI is helping the financial industry to streamline and optimize processes ranging from credit decisions to quantitative trading and financial risk management. In addition, advanced machine learning technology is being used to help organizations to improve customer experience and to enhance their market share.

Furthermore, it provides various solutions to the baking sector to replace routine manual work with automation and to increase productivity. In addition, AI and advanced machine learning help in reducing credit default frauds by monitoring transactions to detect suspicious transactions with compliance concerns.

Improvement in data collection technology among the banks and financial institutions positively impacts the growth of the market. In addition, an increase in investment by BFSI companies in AI and machine learning and a rise in customer preferences for personalized financial services boost the growth of the market across the globe.

However, factors such as the higher deployment cost of AI and advance machine learning and lack of skilled labor are limiting the growth of the market. On the contrary, the surge in the adoption of modern applications in the BFSI sector is expected to offer remunerative opportunities for the expansion of the market during the forecast period.

The global AI and advance machine learning in BFSI market is segmented into component, deployment model, enterprise size, application and region. Depending on the component, the market is segregated into solutions and services.

On the basis of deployment model, it is categorized into on-premise and cloud. Depending on enterprise size, it is fragmented into large enterprises and SMEs.

Based on application, the market is divided into fraud & risk management, customer segmentation, sales & marketing, digital assistance and others. Region wise, the market is studied across North America, Europe, Asia-Pacific, and LAMEA.

The key players profiled in the AI and advance machine learning in BFSI market analysis are:

These players have adopted various strategies to increase their market penetration and strengthen their position in the industry.

Key Market Segments

By Component

By Deployment Model

By Enterprise Size

By Application

By Region

Key Topics Covered:

CHAPTER 1: INTRODUCTION1.1. Report description1.2. Key benefits for stakeholders1.3. Key market segments1.4. Research methodology

CHAPTER 2: EXECUTIVE SUMMARY2.1. Key findings2.2. CXO perspective

CHAPTER 3: MARKET OVERVIEW3.1. Market definition and scope3.2. Key forces shaping global artificial intelligence and advanced machine learning in BFSI market3.3. Case studies3.3.1. CargoSmart adopted Tibco advance analytics solution for improving its decision-making capability by using real-time analysis3.3.2. Honeywell International Inc. adopted data and business analytics platform of Expedien Inc. to increase productivity, lower risk costs, accelerate growth, and lower risk of organizations3.4. Market dynamics3.4.1. Drivers3.4.1.1. Increase in investment by BFSI companies in AI and machine learning3.4.1.2. Increasing preferences for personalized financial services3.4.1.3. Increase in collaboration between financial institutes and AI & machine learning solution company3.4.2. Restraint3.4.2.1. Higher deployment cost of AI and advanced machine learning3.4.2.2. Lack of skilled labor3.4.3. Opportunity3.4.3.1. Increase in government initiatives and growth in investments to leverage AI technology3.5. Market evolution/industry roadmap3.6. Impact of government regulations on the global artificial intelligence and advanced machine learning in BFSI market3.7. COVID-19 impact analysis on AI and Advanced Machine Learning in BFSI market3.7.1. Impact on market size3.7.2. Consumer trends, preferences, and budget impact3.7.3. Economic impact3.7.4. Strategies to tackle the negative impact3.7.5. Opportunity window3.8. Key future initiatives3.8.1. Product launches

CHAPTER 4: GLOBAL ARTIFICIAL INTELLIGENCE & ADVANCE MACHINE LEARNING IN BFSI MARKET, BY COMPONENT4.1. Overview4.2. Solution4.3. Service

CHAPTER 5: GLOBAL ARTIFICIAL INTELLIGENCE & ADVANCE MACHINE LEARNING IN BFSI MARKET, BY DEPLOYMENT MODEL5.1. Overview5.2. On-premise5.3. Cloud-based

CHAPTER 6: GLOBAL ARTIFICIAL INTELLIGENCE & ADVANCE MACHINE LEARNING IN BFSI MARKET, BY ENTERPRISE SIZE6.1. Overview6.2. Large enterprise6.3. SMEs

CHAPTER 7: GLOBAL ARTIFICIAL INTELLIGENCE & ADVANCE MACHINE LEARNING IN BFSI MARKET, BY APPLICATION7.1. Overview7.2. Fraud & Risk Management7.3. Customer Segmentation7.4. Sales & Marketing7.5. Digital Assistance7.6. Others

CHAPTER 8: GOBAL ARTIFICIAL INTELLIGENCE & ADVANCE MACHINE LEARNING IN BFSI MARKET, BY REGION8.1. Overview8.2. North America8.3. Europe8.4. Asia-Pacific8.5. LAMEA

CHAPTER 9: COMPETITIVE LANDSCAPE9.1. Key players positioning analysis, 20209.2. Competitive dashboard9.3. Top winning strategies

CHAPTER 10: COMPANY PROFILE10.1. Amazon Web Services, Inc.10.2. BigML, Inc.10.3. Cisco System Inc.10.4. FAIR ISAAC CORPORATION10.5. HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP10.6. INTERNATIONAL BUSINESS MACHINES CORPORATION10.8. MICROSOFT CORPORATION10.9. RapidMiner, Inc.10.10. SAP SE10.11. SAS INSTITUTE INC.

For more information about this report visit https://www.researchandmarkets.com/r/nzvud5

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Global AI and Advance Machine Learning in BFSI Market Report (2021 to 2030) - GlobeNewswire