1.      Automation of Jobs

Automating jobs through AI is one of the most impactful economic trends today. Numerous routine cognitive and manual occupations like billing, record-keeping, and quality control are being automated by intelligent algorithms and systems. This inevitably displaces some human workers whose roles are taken over by AI. However, automation also creates new jobs for developing, deploying, and overseeing these AI systems. As AI chatbots handle straightforward customer service queries, human agents are freed to focus on solving more complex issues. While workforce disruptions occur in the short term, the longer-term outlook is more optimistic. As AI takes over routine tasks, people can shift to higher-value work requiring strengths like creativity, empathy, and problem-solving. This enables human workers to become more productive and find greater meaning in their work.

2.      Efficiency Gains

Organizations implementing AI to optimize operations are realizing substantial efficiency gains and cost savings. Supply chain management is one area being transformed through AI predictive analytics. By more accurately forecasting demand, waste, and shortages are reduced. Organizations are also achieving predictive maintenance, where AI systems detect faults and problems before equipment failures or breakdowns happen. This prevents costly unplanned downtime. Other areas being streamlined by AI include reporting, pricing, logistics, and regulatory compliance activities. As organizations become more efficient, they can pass on savings through lower prices or reinvest them to improve offerings. This provides better value to customers. Efficiency unlocks new opportunities and frees resources to develop innovative products and services.

3.      Personalization

Sophisticated AI algorithms, that you can learn about on https://www.sap.com/products/artificial-intelligence/what-is-artificial-intelligence.html, enable businesses to personalize recommendations, content, and experiences for each customer. Online platforms like Netflix and Amazon use AI systems to analyze individual preferences and tailor suggestions for media, products, and services. Beyond personalized recommendations, AI chatbots engage website visitors in conversational interactions. They assess interests based on dialog and dynamically offer customized advice. Companies collate extensive customer data and feedback to continuously refine and improve personalization. By boosting relevance and loyalty, personalization powered by AI provides a strong competitive advantage. It shows that firms deeply understand and care about satisfying the unique needs of every customer. Personalized offerings matched to specific tastes and requirements help drive purchasing and long-term brand relationships.

4.      Enhancing Business Productivity

Organizations across sectors have only begun tapping into AI’s immense potential to boost efficiency and reduce costs. By automating repetitive tasks, AI systems allow employees to focus on higher-value work. Intelligent algorithms can also analyze operations data to identify waste and optimization opportunities. For example, AI can tweak production schedules to minimize changeover time between product lines. Dynamic pricing algorithms can maximize profit margins based on fluctuating supply and demand. Predictive maintenance applications detect potential equipment failures before they occur, minimizing downtime. According to McKinsey, AI techniques like machine learning can deliver productivity gains of up to 30% across industries. As more companies implement AI-powered automation, they may see dramatic gains in productivity, speed, and quality. However, the workforce implications of AI must be responsibly managed through training and transition programs.

5.      Transforming Patient Care

In healthcare, AI holds tremendous promise to enhance patient outcomes and potentially save lives. AI systems can analyze medical images and detect anomalies earlier than the human eye. Machine learning algorithms can also comb through patient records to identify risk factors and suggest preventative steps. AI chatbots provide faster triage and symptom checks without needing appointments. According to Accenture, key clinical health AI applications can potentially create $150 billion in annual savings by 2026. However, AI in healthcare requires immense diligence to ensure safety and efficacy. Extensive real-world testing is essential to validate performance. Healthcare organizations must also implement safeguards around data privacy and security. If thoughtfully developed and validated, healthcare AI could revolutionize medicine by offering more accessible, personalized, and effective care. However, it will require partnerships between technology and medical experts to ensure AI is rigorously validated.

6.      Funding AI Research

Global governments are providing significant funding to advance AI research, commercialization, and adoption. Major national science agencies in the U.S., like the NSF, offer grant programs to catalyze AI innovation. The   Union is mobilizing a big amount through 2027 to position itself as an AI leader. China is also investing billions, aiming to dominate AI globally within the next decade. Government financing flows to high-potential research initiatives exploring critical future applications of AI. It also enables translating innovations from the lab to the marketplace by supporting startups. These investments aim to realize AI’s economic potential while positioning countries and regions as hubs of expertise and progress. They create spillover benefits across industries and society. Targeted funding in foundational research and commercialization is crucial to keep pace with rapid advances in AI.

7.      Modernizing Regulations

Outdated regulations are being overhauled to permit testing and adoption of transformative AI technologies. For example, governments are enacting clear guidelines and rules to allow autonomous vehicle trials and eventual mainstream use. Regulations are also adapting to enable drone delivery flights and other AI applications. Policies around acquiring, sharing, and using data are being updated to advance innovation while still protecting privacy. Adjusting regulations in measured ways allows businesses and researchers to fully explore promising AI applications. It paves the path for emerging technologies to be deployed at scale. However, modernized regulations still need provisions to manage risks. AI oversight mechanisms must be instituted along with digital ethics standards. With thoughtful policy evolution, the potential of AI can be harnessed while safeguarding the public interest.

8.      Retraining Workers

As AI transforms skill demands, governments, community colleges, nonprofits, and corporations are providing retraining programs. These initiatives help workers displaced by automation pivot into new, stable careers requiring different capabilities. Retraining   include offerings in data analysis, user experience design, cybersecurity, machine learning, and other high-demand areas. Beyond technical skills, they cultivate adaptability to handle ongoing workplace changes. Retraining enables workers to remain professionally competitive, transition into emerging roles, and address talent shortages. Rather than being displaced, workers can actively reorient their careers. Companies implementing automation have a responsibility to invest in upskilling employees, too. Retraining initiatives create more inclusive economic growth where the benefits of AI are widely shared.

Conclusion

In closing, from streamlining operations to tailoring products, AI is fundamentally altering business and labor. However, thoughtful policies and strategies can maximize its economic benefits and mitigate challenges. AI should be shaped to augment human capabilities, not substitute them. Companies must also foster agility to keep pace with intensifying competition. Individuals should likewise pursue continuous learning as work evolves. With informed, proactive efforts, AI can boost prosperity, efficiency, and innovation.

The post The Impact of AI on The Global Economy: 8 Trends And Initiatives appeared first on Fintech News.

This article will review some of fintechs impact on gaming in Canada. Read on to gain more insights.

Enhanced User Experience

Canada has an adverse gaming industry. Additionally, their fintech culture is on point. The companies offering these services aim to ensure users can seamlessly transact, offering them convenience.

When browsing various gaming platforms, you’ll often run into a prompt requiring you to make a purchase. Doing so through segregated financial means like banks sometimes requires you to leave the game and transact.

However, through fintech, gamers don’t have to worry about this. Transacting through these integrated platforms reduces the friction during execution, enhancing the general user experience.

Data Security and Fraud Prevention

In the modern era, cybercrime is on the persistent rise due to the dynamic nature of technology. Fintech aims to provide Canadian gamers with a safe and secure transacting experience. Therefore, several innovations have been conjured to curb this problem.

For instance, data encryption has become a popular Canadian gaming trend. Through encryption, users have reported significant drops in cybercrime rates. Additionally, other fintech innovations like two-factor authentication that require users to provide more than one means of verification come into play.

Fintech companies also aid in preventing fraud. Identity theft has become a common practice in the gaming niche. These companies collecting players’ data and sensitive information may draft methods to keep their users safe.

Blockchain and Cryptocurrency

Cryptos are an asset that the world can no longer ignore. With its massive growth over recent years, cryptos are proving helpful in several sectors, including gaming. Fintech has enabled the realization and actualization of integrating this digital currency into gaming platforms. It is gaining traction, with 1.6 million people owning cryptos in Canada.

Cryptos, through fintech, has influenced the creation of games like Cryptokitties and Lost Relics, giving Canadian gamers a chance to trade in the digital marketplace. Online gamers with a liking towards wagering are also represented, as Bitcoin casino sites like Izzi and Bodog have some of the best payouts for online casinos in Canada.

Additionally, transacting through cryptos ensures safety in operation. Fintech has enabled payment systems to be more secure. However, the security is more fortified with cryptos because they can be traced.

Investment and Funding

The Canadian gaming scene has thrived due to the free flow of money. Sometimes, gaming companies or operators need funds to effectively toss their weight around on a global scale. Fintech enables this by streamlining investment opportunities and offering new funding avenues.

Most fintech innovations use machine learning technology, allowing them access to data analytic tools. Through these advancements, they can find suitable investment avenues. These inventions are significant to the Canadian gaming industry because they help developers identify trends in gaming that can help them be more sustainable.

Moreover, several fintech-enabled crowdfunding avenues like Kickstarter and Indiegogo help gaming businesses more so startups to operate effectively. These avenues aim to break away from traditional forms of funding through this means.

Cross-Border Transactions

The Canadian gaming industry collaborates with the rest of the world. Through this form of trading, cross-border payments are necessary. Fintech has played an enormous role in ensuring that this possibility actualizes.

Business people do not need to frequent banks to make such transactions. You can wire funds internationally with a compatible mobile phone, internet connection, and the necessary details.

Furthermore, fintech has aided in pushing the Canadian gaming sector more globally. It has managed to do so by drafting cost-favorable methods for transferring money internationally and facilitating currency exchange.

Payment Processing and In-Game Purchases

Fintech is a crucial mediator when considering payment processing and executing in-game purchases. Canada has a diverse and sustainable fintech ecosystem facilitating payment processing. The Canadian gaming niche has thrived through inventions like digital wallets, mobile banking, and payment avenues.

Traversing the realm of digital transactions can be confusing at first. Fintech innovations are constantly evolving to try and make it simpler. Gamers can easily purchase their customized skins, characters, and battle passes when playing.

Regulatory Considerations

As with any niche, regulatory compliance is inevitable. Fintech industries operating in Canada must abide by the jurisdictional laws. This measure ensures smooth operation and ensures gamers reap maximum utility.

This element will, however, vary with time as laws, especially regarding technology, are dynamic. For instance, during their initial release, cryptos faced friction and resistance. However, after proper structuring of legislation, countries became more lenient.

Conclusion

Fintech is a global phenomenon that collaborates the financial and technological aspects to offer more advantages. In gaming, this is evident as you’ll encounter several cryptocurrencies aiding transactions, cross-border payments facilitating trade, and crowdfunding platforms that rose due to fintech.

The post Unveiling Fintech’s Impact on Canada’s Gaming Sector appeared first on Fintech News.

Link:https://www.paymentsjournal.com/using-ai-to-combat-financial-crime-in-real-time-payments/

Source: https://www.paymentsjournal.com

The post Using AI to combat financial crime in real-time payments appeared first on Fintech News.

Ultimately our goal here is to offer readers the pros and cons of investing in AI today.  Yet this is obviously not personalized investment advice of any kind.  Investors should consult with an investment professional before making any investment much less one in such a novel and possibly speculative technology like AI.   With that, let’s start out with the bear case on AI.   

For that we will turn to Kailash Concepts.  Founded by a team of experienced money managers and a leading academic in the field of behavioral finance, Kailash’s research consistently pointed the evidence that the speculative mania that gripped America in 2020 would end badly.  We think their predisposition for prudence and caution makes them a terrific place to start.

They recently posted a missive titled Out with the Old, In With the New: What ChatGPT Means for You.   Written in simple language they make a compelling case for why not to invest in AI today.  The piece reminds readers that change and disruption in tech is a recurring feature of the investment landscape.  Their view is that the benefits of technology tend to flow to society over time rather than to the investors and their website is loaded with free charts highlighting the timeless lessons of history.

As is often the case with new technologies, the bulls currently have the upper hand.   Amazed with this new technology, users are clamoring to invest in AI.  Here’s why: 

AI has the potential to disrupt many industries

AI is already being used in many industries, such as healthcare, finance, retail, and transportation, to improve operations and drive innovation. For example, AI can help doctors diagnose diseases, assist traders in making better investment decisions, and enhance customer experiences in retail. As AI technology continues to advance, we can expect to see even more industries being disrupted, creating new investment opportunities.

AI can improve efficiency and reduce costs

AI can automate routine tasks and decision-making, freeing up time for humans to focus on more complex and creative tasks. This can lead to increased efficiency and productivity, which can in turn reduce costs for businesses. As more businesses adopt AI, there is a growing demand for AI solutions, providing an opportunity for investors to invest in companies that offer AI products and services.

Large language models have the potential to change how we communicate

Large language models, such as GPT-3 and BERT, are AI systems that can generate human-like language. These models have the potential to change how we communicate, as they can be used for natural language processing, translation, and even creative writing. As the technology advances, we may see more applications of large language models in various industries, such as education, media, and marketing.

AI can lead to more personalized experiences

One of the benefits of AI is its ability to process and analyze large amounts of data quickly and accurately. This can enable businesses to provide more personalized experiences to their customers, such as customized product recommendations or personalized marketing messages. As businesses increasingly adopt AI, investors can expect to see more investment opportunities in companies that offer AI-powered personalization solutions.

AI can help address some of the world’s biggest challenges

AI has the potential to address some of the world’s biggest challenges, such as climate change, healthcare, and poverty. For example, AI can be used to improve energy efficiency, develop new drugs, and provide access to education and healthcare in remote areas. Investing in companies that are working on these solutions can not only provide financial returns, but also make a positive impact on the world.

AI is still in the early stages of development

Despite the progress that has been made in AI, the field is still in the early stages of development. There is still much to be discovered and many new applications of AI that have yet to be explored. As such, there is a lot of room for growth and innovation in the field, creating new investment opportunities for investors who are willing to take a long-term view.

AI is a competitive advantage for businesses

As more businesses adopt AI, it is becoming a competitive advantage. Companies that are early adopters of AI are able to leverage the technology to improve their operations and gain a competitive edge over their rivals. As such, there is growing demand for AI solutions, which can provide opportunities for investors to invest in companies that are at the forefront of AI innovation.

In conclusion, investing in AI and other large language models using AI can provide investors with an opportunity to tap into the growth potential of this rapidly evolving field. AI has the potential to disrupt many industries, improve efficiency and reduce costs, change how we communicate, lead to more personalized experiences, help address some of the world’s biggest challenges, and provide a competitive advantage for businesses. However, it is important to note that investing in any industry brings risks.  

And as a reminder: we started this piece with Kailash’s work for a reason.  The risks to investing in AI and any other novel technology are many and myriad.  The highlight reel above is why investors are excited to invest in AI but that does not mean they will actually materialize much less generate profitable investments!  

The post Artificial Intelligence (AI) and Large Language Models: A Comprehensive Guide for Investors appeared first on Fintech News.

Define your goals – Consider Getting A Degree In BI

If you’re feeling like it’s time to make a change and reach for something more in your life, consider getting a degree in Business Intelligence (BI). It is one of the fastest-growing segments of the job market, with many attractive features that can help propel your career. Having a diploma in BI will teach you the key skills for utilizing BI reporting tools to perform data analysis and data management, allowing you to maximize success for yourself and any organization that you work with. An online DBA in business intelligence can be incredibly demanding and offer a challenging learning experience, but the benefits are wide-ranging – with great potential as an impressive addition to your qualifications. Make sure you choose the right school and program that fits your individual needs and goals before embarking on this educational journey.

Do Your Research – Learn About The Different Aspects Of Business Intelligence So That You Can Make Informed Decisions

Making an informed decision requires doing your due diligence. As a business person, you should take the time to research and learn about the different aspects of business intelligence. By understanding how each component works in connection with the others, you will gain invaluable insight into how to identify potential opportunities for your organization. Business intelligence provides an opportunity to transform raw data into actionable information that can be used for strategic planning and decision-making. This can lead to improved productivity, better customer service, enhanced business operations, and increased profitability. Remember – taking the time and effort to do your research on business intelligence is well worth it when it comes to making smart decisions that will benefit your organization.

Get Organized – Create A System That Will Help You Collect And Track Data Effectively

Creating a system to collect and track data effectively can feel overwhelming, but it doesn’t have to be. Start by gathering all the relevant data you need – think of it as the raw material for your organized system. Next, create a quick reference guide that includes instructions on how to use the data. This will save you time down the line when you look back at it. Afterward, find a way to make the data easily accessible. Whether that’s by labeling physical documents or creating online folders and spreadsheets is up to you, but make sure you know where everything is stored. Having an efficiently organized system of collecting and tracking data may seem like a huge hurdle at first, but taking small steps one at a time will help ease some of the stress associated with managing large amounts of information.

Implement Change – Use The Insights From Your Data To Improve Your Business Operations

Managing change within business operations is often a difficult but necessary task. Utilizing the insights from data can help to take out some of the guesswork, allowing companies to make informed decisions and stay ahead in an ever-evolving environment. By crunching numbers and analyzing patterns, managers can identify potential blind spots and adjust course accordingly. Using analytics to gain insight into customer behavior and track progress can also help businesses reimagine their strategies for growth – what used to require manual review now takes just minutes. Data-driven decisions allow organizations to rapidly modify their processes to ensure they stay agile in a competitive marketplace. All in all, using insights from data can be an invaluable tool for managing and implementing successful change in any business.

Monitor Results – Keep Track Of The Impact Of Your Changes So That You Can Continue To Optimize Your Process

Monitoring the results of your process is an essential step to optimizing it over the long haul. For instance, if you are just starting up a blog and want to constantly improve the readership, you should closely track your results to identify which approaches you should always be striving for. That said, being able to interpret and analyze what your results mean takes practice. Understanding how specific changes impact user engagement can help provide deeper insights into why certain approaches work better than others. All this contributes to discovering the right balance between the time invested and the desired goals, leading to more efficient use of resources while creating something valuable at the same time.

Business intelligence is an invaluable tool for any business’s success. It can help you uncover new insights and make your operations smarter, more efficient, and more profitable. With proper planning, careful analysis, and structured implementation, your organization can gain a much better understanding of itself, allowing it to plan more effectively and strategically. Even if you don’t have any formal business intelligence training or experience, with the right resources you can still take advantage of the power and potential that BI offers. Taking the time to define your goals, do your research, get organized, and monitor results will pay off, in the long run, it may seem daunting at first but the effort is well worth it!

The post Guidelines On How To Become Successful In Business Intelligence appeared first on Fintech News.

Due to its importance and undeniable effectiveness, image classification is becoming more and more available and advanced, offering more precision and reliability of its results. As today satellite imagery analysis is nothing new to numerous industries, its classification finds use in a long list of applications, including crop monitoring, forest cover mapping, soil mapping, land cover change detection, natural disaster assessment, and much more. For example, crop classification using remote sensing is a great opportunity for agricultural players to plan crop rotation effectively, estimate supply for certain crops, and more.

But how does satellite imagery classification actually work? Technology is the answer. More specifically — machine learning, artificial intelligence, and most importantly deep learning. Let’s get into more detail to see how the “magic” happens, enabling us to see maps with different objects possessing specific visual characteristics.

Satellite Imagery Classification Using Deep Learning

With hundreds of observation satellites orbiting the Earth and new satellites being launched, the amount of imagery they produce is growing constantly. However, to make use of these images across different industries and applications, like environmental monitoring, city planning, or agriculture, they need to be classified.

The methods of satellite image classification can be put into four core categories depending on the features they use: object-based methods, unsupervised feature learning methods, supervised feature learning methods, and manually feature-based methods. Today, supervised deep learning methods have gained the biggest popularity among remote-sensing applications, especially when it comes to land use scene classification and geospatial object detection.

Deep Learning And How It Works

Deep learning can be viewed as a form of machine learning. Self-learning and improvement of program behavior occurs as a result of the execution of computer algorithms. But classical machine learning algorithms use fairly simple concepts, while deep learning works with artificial neural networks. These networks are designed to mimic the way humans think and learn.

Advances in big data analytics have made it possible to create large and complex neural networks. Thanks to them, computers can observe, learn, and respond to complex situations even faster than humans. Today, deep learning helps classify images, translate texts from one language to another, and recognize speech.

Deep learning is based on artificial neural networks consisting of many layers. In a Deep Neural Network (DNN) each layer can perform complex operations of representation and abstraction of images, sound or text. One of the most popular types of deep neural networks is known as convolutional neural networks (CNN). CNN combines learned features with input data and uses convolutional 2D layers, making this architecture perfectly suited for processing 2D data, such as images.

CNN and Satellite Imagery Classification

Convolutional neural networks are particularly useful for finding patterns in images to recognize objects, faces, and scenes. They learn directly from images, using patterns to classify images and eliminating the need for manual feature extraction. The use of CNNs for deep learning has become more popular because of three important factors:

• CNNs eliminate the need for manual feature extraction
• CNNs produce state-of-the-art recognition results
• CNNs can be retrained to perform new recognition tasks, allowing for leveraging existing networks.

CNNs eliminate the need for manual feature extraction, so there is no need to determine the features used to classify images. CNNs work by extracting features directly from images. The relevant features are not pre-trained; they learn while the network is trained on a set of images. This automatic feature extraction makes deep learning models very accurate for computer vision tasks, such as object classification.

CNNs learn to detect different features in an image using dozens or hundreds of hidden layers. Each hidden layer increases the complexity of learned image features. For example, the first hidden layer may learn to detect edges, and the last layer may learn to detect more complex shapes specifically adapted to the shape of the object we are trying to recognize.

Overall, it’s hard to overestimate the role of deep learning in imagery classification. Thanks to modern advancements in AI algorithms, we can pull out more and more of invaluable insights from satellite pictures, increasing the effectiveness and sustainability of many industries on Earth.

The post Satellite Imagery Classification Using Deep Learning appeared first on Fintech News.

Machine learning is a term that is thrown around a lot in the business world today. But what does it mean? And more importantly, what can entrepreneurs do to take advantage of it? In this blog post, we will answer these questions and more! We will discuss what machine learning is, how it works, and some of the benefits that it can offer businesses. We will also provide tips on how entrepreneurs can start using machine learning in their businesses!

How Does Machine Learning Work and What are Some of Its Benefits for Businesses?

Machine learning is where computers are taught how to get information from data, without being explicitly programmed. This process can be used to make predictions or recommendations based on past events. Machine learning can be used for a variety of tasks, such as fraud detection, image recognition, and identifying trends in customer behavior in businesses. Click here to learn more about machine learning businesses.

Machine learning can be categorized into three: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is where the algorithm is given a labeled dataset, which means that there is a correct answer for each example in the dataset. The algorithm then learns to map the input data to the corresponding output label. Unsupervised learning is where the algorithm is given an unlabeled dataset, which means that there are no correct answers for each example in the dataset.

The algorithm must then learn to find patterns and structure in the data on its own. Reinforcement learning is where the algorithm is given a reward signal that it can use to adapt its behavior. The algorithm learns by trial and error and can improve its performance over time by receiving feedback on its predictions.

Machine learning algorithms are constantly improving as they are given more data. This is why it is important to keep your dataset up to date. If you have an outdated dataset, then your machine learning algorithm will not be able to accurately predict the labels for new data.

It is also important to have a large enough dataset. If your dataset is too small, then the machine learning algorithm will not be able to learn the structure of the data and will not be able to generalize to new data. Having a larger dataset also allows you to train your machine learning

Why Should Entrepreneurs Care About Machine Learning?

As machine learning becomes more widespread, businesses are starting to take notice. But what is machine learning? And why should entrepreneurs care about it?

There are a few reasons why machine learning is important for businesses:

• Machine learning can help you automate repetitive tasks. For example, if you’re constantly having to analyze customer data manually, you can use machine learning to create a system that does it for you. This frees up your time so you can focus on other things.

• Machine learning can improve your decision-making. By analyzing data and identifying patterns, machine learning can help you make better decisions about your business. For example, you might use machine learning to decide which products to stock or what prices to set.

• Machine learning can help you personalize your product or service. By understanding your customers’ behavior, you can use machine learning to tailor your product or service to their needs. This could be anything from recommending products they might like to show them targeted ads.

Entrepreneurs should care about machine learning because it can help them automate tasks, improve decision-making, and personalize their products or service. If you’re not already using machine learning in your business, now is the time to start!

What Industries Are Expected To Be The Biggest Beneficiaries of Machine Learning in The Future?

The deployment of machine learning is expected to have a profound impact on several industries in the coming years. Here are three industries that are expected to be the biggest beneficiaries of this technology:

1. Healthcare: Machine learning will enable healthcare organizations to better detect and diagnose diseases. It will also help them develop better and more accurate diagnostic tools to improve patient care through personalized medicine and help manage large amounts of data more effectively.

2. Finance: Machine learning will help financial institutions reduce fraud and improve customer service by developing better and more accurate credit scoring models to help financial institutions comply with regulations more efficiently. Additionally, it will enable them to make better decisions about investments.

3. Retail: Machine learning will allow retailers to provide more personalized recommendations to customers and improve inventory management. Additionally, it will help them detect fraudulent activities such as return fraud and coupon abuse.

4. The automotive industry: This is another sector that is expected to see major benefits from machine learning in the future. Machine learning is being used to develop autonomous vehicles to improve the efficiency of manufacturing processes and to help diagnose and repair problems more quickly.

5. Logistics: Machine learning can help with things like route optimization, predicting demand, and even reducing emissions. Here are three more ways that machine learning can benefit the logistics industry in the future:

   First, machine learning can help with route optimization. By understanding traffic patterns and using historical data, machine learning algorithms can help identify the best routes for trucks to take. This not only saves time but also reduces fuel consumption and emissions.

   Second, machine learning can be used to predict demand. This is especially important for industries that have seasonal fluctuations in demand, such as retail or agriculture. By using machine

6. Power Industry: The potential benefits of machine learning for power industries are vast. For example, predictive maintenance is a significant opportunity for machine learning to improve operational efficiency and reduce downtime in the power sector. By analyzing data collected from sensors, machine learning can identify patterns that indicate when equipment is likely to fail.

Machine learning is a rapidly evolving field with endless potential applications. These are just a few of the many industries

Challenges Businesses Face When Implementing Machine Learning Into Their Operations?

One of the challenges that businesses face when implementing machine learning into their operations is data quality. For machine learning algorithms to work effectively, they need high-quality data sets to learn from. This can be a challenge for businesses because collecting and maintaining high-quality data sets can be costly and time-consuming.

Another challenge that businesses face when implementing machine learning is finding qualified personnel. Machine learning is a relatively new field and there is a shortage of qualified professionals who can develop and deploy machine learning models. This can be a challenge for businesses because it can be difficult to find and retain talent.

Also, managing expectations is another issue. Machine learning is often hyped up and sold as a silver bullet that can solve all of a business’s problems. However, machine learning is not a panacea and businesses need to manage expectations around what they can and cannot do.

If businesses can overcome these challenges, then they will be well on their way to successfully implementing machine learning into their operations. Machine learning has the potential to provide significant benefits to businesses, but it is important to be aware of the challenges involved to set realistic expectations.

How Can Entrepreneurs Make Sure They’re Taking Advantage of All That Machine Learning Has To Offer?

As an entrepreneur, you are always looking for ways to stay ahead of the competition. One way to do this is by adopting machine learning into your business strategy. There are a few ways that entrepreneurs can make sure they’re taking advantage of all that machine learning has to offer. These include.

1. Work with data scientists who can help you identify which problems are best suited for machine learning and how to frame them correctly.

2. Use off-the-shelf machine learning models when possible. This will save you time and money that you would otherwise spend on building your models

3. Make sure you have a good understanding of the business problem you’re trying to solve. This will help you identify which machine learning algorithms are best suited for the task.

4. Adopt machine learning and use it to automate repetitive tasks so you can focus on more important things

5. Analyze customer data to better understand their needs and wants

6. Use predictive analytics to make better decisions about where to allocate your resources

7. Identify trends in your industry so you can be the first to capitalize on them

8. Monitor social media for mentions of your brand or products so you can quickly address any negative sentiment

9. Create targeted marketing campaigns using insights gleaned from customer data

10. Stay ahead of the competition by monitoring their activity and predicting their next move

11. Use machine learning to monitor your own business for inefficiencies so you can quickly address them

By following these tips, entrepreneurs can make sure they’re taking advantage of all that machine learning has to offer. Machine learning is a powerful tool that can help businesses improve their products and services. But it’s important to understand how to use it correctly to get the most out of it.

Conclusion

Adopting machine learning into your business strategy is a surefire way to stay ahead of the competition. By automating repetitive tasks, analyzing customer data, and using predictive analytics, you can gain a competitive edge that will help you grow your business.

The post What entrepreneurs should know about machine learning appeared first on Fintech News.

Why AI Explainability Matters

It is important for fintech companies to understand the AI decision-making processes without relying on them blindly. The emergence of explainable AI can help humans understand and explain ML algorithms and neural networks.

This makes it imperative for fintech companies to monitor and manage model results to improve AI explainability while measuring the business impact of using such algorithms in financial risk management and audit. Explainable AI helps strengthen end-user trust and productive use of AI. It also reduces compliance, legal, security, and reputational risks for production AI.

Developing an AI model for the use of the fintech industry can help forecast events and rate transactions based on prior patterns. Once the model is in use, it receives millions of data points, then interacts in billions of different ways to produce results quicker than any combination of human effort.

The danger is that the ML model can be producing these results in a closed system that is only understood by the group that created the model from scratch. In 2021, 32% of financial executives responding to the LendIt annual study on AI cited that the lack of explainability is their second highest concern after regulation and compliance.

How Explainable AI Works

As AI becomes integral in the fintech industry, explainability has become important to building customer trust. Although “black box” solutions allow users to know the input and the final output, it obscures the process of how a decision came to be. XAI also called the “white box” model, helps transmit decision-making information to outputs and makes them obvious to FI’s users. These exhaustive data are then examined by users to explain and validate the results.

There are some setup methods for XAI technology. Predictive accuracy addresses technical needs, while decision comprehension addresses human needs.

1. Prediction Accuracy

Prediction accuracy can be determined by running a simulation and comparing the XAI output with the results in the validation dataset.

2. Decision-making Comprehension

This is the human factor.  Explainability can be achieved by training teams to work with AI so they can understand how and why AI makes decisions.

Explainable AI gives fintech companies more clarity on their AI governance while assisting them in providing transparency and building trust with their customers. XAI comes into play to make humans comprehend AI models without compromising performance or prediction accuracy.

How to Implement Explainability in Your App

The process of implementing XAI across fintech organizations involves many aspects and steps, including the development of models, interaction with various stakeholders, governance procedures, and involvement of outside vendors. For example, the following objectives should be at the forefront of banks’ XAI implementation:

1. XAI should make it easier to comprehend whether features or interactions had an impact on model predictions as well as the processes a model has taken to come to a conclusion.
2. Explanations should detail a model’s benefits and drawbacks as well as potential future behavior.
3. Customers should be able to comprehend explanations, which should be presented in a simple and intuitive manner according to the preferred language of the target audience and their technical proficiency.
4. XAI methods should reveal insights on model behavior, in addition to how an organization will use the results.

To kickstart a project and turn a vision into reality, fintech companies need the support of an experienced software development team. For example, at MobiDev, the financial software development process always starts with the discovery phase which helps to understand the project requirements and create a clear roadmap for further product development. Then AI engineers consult on how to apply machine learning algorithms to achieve project goals.

Implementing XAI models as a formal regulation helps fintech companies get closer to accomplishing these objectives. From the pre-modeling phases to the monitoring and evaluation phases following the deployment, this will entail implementing new policies and approaches.

Challenges and the Future of Explainable AI

Limitations and Challenges

Although XAI research has witnessed a significant rise, fintech companies still experience conundrums introducing explainability into the AI pipeline. Some explanations might not support adjustments that can be made to interest rates, repayment plans, and credit limitations, thereby, neglecting consumers’ preferences for various loan arrangements.

Some organizations have voiced fear that explainability might enable their competitors to reverse-engineer their ML models, thereby disclosing the “secret element” behind their proprietary algorithms. Also, they have drawn attention to the possibility that XAI could make it simpler for outsiders to manipulate their models or launch aggressive attacks that break them. Without looking at the reasoning behind an AI’s conclusions, it is challenging to say whether it is trustworthy or not.

The Future of XAI

Despite all limitations, XAI technology is seeing exponential growth in the fintech industry and it’s only getting started. A survey by NMSC report estimates that the worldwide XAI market would be worth $4.4billion in 2021 and $21.0billion in 2030, with a CAGR of 18.4% from 2022 to 2030.

One of the major reasons that prevent many people from embracing AI is the lack of explainability and trust. But the gap has been bridged, thanks to explainable AI. Fintech companies can now comprehend every form of data-driven decision-making. When working with an experienced team of AI developers, all these challenges can be overcome and you will get a quality AI-powered solution.

Author:

Anastasiia Molodoria

AI/ML Team Leader at MobiDev

https://mobidev.biz/our-team/anastasiia-vynychenko-ai-mobidev

The post Explainable AI in Financial Risk Management appeared first on Fintech News.

By FintechNews staff

-A chatbot is a customer experience tool. It can help improve and customize the user experience at every stage, saving banks and finance brands time and money in the process.

– Chatbots not only allow customers to manage requests in a faster and more efficient way, but they also act as a listening channel from which e can better understand our customers.

-Improving the efficiency of customer service, minimizing human error and resolving customer queries quicker, has a major impact on operational costs. In fact, according to a Juniper study, the use of chatbots will save banks up to $7.3 billion worldwide by 2023. This represents a time saving of 862 million hours, or almost half a million years of work. According to Gartner, by 2020 chatbots will be handling no less than 85% of all customer service interactions.

-The functions of a finance or banking chatbot are nearly endless. A few of the most helpful applications include: Automated, personalized customer support, 24/7 , ease of use, cost saving, advise and information, Conversational interface, audience segmentation, Customer feedback collection, New account generation.

-Here’s are two chatbot trends that will shape better customer service outcomes in 2022.

. Well-designed omnichannel bots: People use many different communication channels on a daily basis: from email, to social media, to messaging apps such as WhatsApp. A recent study reported that companies investing in an omnichannel customer engagement strategy can increase their conversion rates by 47% and enjoy a 90% higher retention rate.

.Payment through chatbots:  More bots will be connected with payment systems such as Paypal and digital wallets, allowing consumers to make payments without ever leaving the messaging platform, improving customer experience.

-Chatbots are and will continue to be a game-changer to enhance front-end services at financial institutions.

The post How chatbots are driving fintech appeared first on Fintech News.

By FintechNews Staff

-Banking and finance are regularly targeted by cyber hackers, so fraud detection is the area where artificial intelligence can increase the protection level.

-Account takeover and identity fraud cost financial institutions $16.9 billion in 2019 alone.

-With the help of AI to analyze data in real time, banks are able to identify suspicious activity and predict risk levels in-the-moment, in order to detect fraud as it is happening

-To prevent fraud, banks and other financial institutions are increasingly relying on sophisticated alternatives that combine Artificial Intelligence (AI) and Machine Learning (ML) technology. A solution like ML is capable of dealing with enormous amounts of data from several sources and knows what the normalized levels of activity are with regard to banking and other financial transactions. Consequently, it can alert the supervisor in case of any deviations from the expected trends.

-This kind of baseline could also be established for interactions with other banking operations or entities. In addition to account owners, fraud can come from merchants and issuers, and their transaction information can be used to train a machine learning model to recognize transactions processing properly.

– Information about devices, the geolocation of users, and even behavioral biometrics are playing the role of additional fuel for analytics.

To go deeper on these subjects: 

–The state of cybersecurity in financial services 

-Transforming Cybersecurity in the Financial Services Industry

-Addressing U.S. cybersecurity concerns through biometrics 

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