Artificial Intelligence in Manufacturing 

Behind every operation that takes place in the manufacturing industry, artificial intelligence plays a major role. AI has the ability to receive different types of data not just from people, but also from machines, sensors, etc., and use it with specifically designed algorithms in order to optimize operations. If we consider the use of artificial intelligence in manufacturing, AI offers multiple ways to improve industrial value. Machine Learning or ML is the most commonly used subset of AI in manufacturing. The process manufacturing industry is one of the highly combative industries with rapidly transforming markets and complex systems. Therefore, the process plants need every single benefit that AI and ML can offer to spur innovation and maximize profits. 

Let us go through this article to know more about the different applications of artificial intelligence in the manufacturing industry. 

Benefits of using AI in the Manufacturing Industry

Implementing Artificial Intelligence has many for the manufacturing industry.  Let us start with the key advantages or benefits AI offers in this area of operation: 

Faster decision making 

With the integration of IIOT (Industrial Internet of Things) with VR OR AR and cloud computing, companies will be able to share simulations, talk about production activities, and pass on live crucial or relevant information no matter wherever their location is. Moreover, the data collected from sensors and beacons help decide customer performance, enable businesses to foresee future requirements, make quick decisions on production, as well as accelerate transfers between manufacturers and suppliers. 

24/7 production  

The working hours of human employees are divided into 3 shifts to ensure smoothness and continuity in production. On the other hand, there are AI-induced robots that work 24/7 without any intervals or breaks. They are designed to enhance productivity and increase business while meeting the demands of customers around the world. 

Safe environment  

Human workers can make mistakes knowingly or unknowingly. Such mistakes may result in uncompromising errors or accidents in the organization. Though AI cannot eliminate the risk factors completely, it can at least minimize or reduce the intensity of errors. The availability of remote access controls eliminates the need for human interventions. Also, ultra-modern sensors allied with IIoT (Industrial Internet of Things) equipment assist in the efficient installation of defense and security guards. 

Opportunity for human employees  

As AI takes up the manual jobs done by human employees, it lifts the weight of time-consuming, tiring, and repetitive work from manual workers. Human employees now have the time to focus more on creative and challenging tasks that actually require their intelligence to drive the business in the right direction. 

Less operational cost  

It is true that setting up AI in industrial businesses requires a huge capital investment but the ROI (return on investment) you can expect from AI is significantly higher. Once intelligent machines start to deal with day-to-day operations, businesses will be able to save lots of functional costs. 

Use Cases of AI in the Manufacturing Industry

As we have discussed the benefits of AI in the manufacturing industry, let us move to its use cases. The use cases of artificial intelligence in manufacturing include: 

Predictive maintenance   

Analyzing sensor data allows manufacturers to detect possible errors and downtime, anticipate the time when machines stop working, and schedule repairs before errors occur. This results in increased efficiency because the functions do not have to be stopped, and minimizes the cost of repairing and replacing failed machines.  


As large amounts of data are produced in security logs due to the manufacturing industry environment, filtering doubtful ones during everyday operations is a big task. Artificial intelligence is capable of identifying fraud, infiltrators, malware, and more on its own, enabling it to deal with modern cybersecurity threats and challenges more rapidly and precisely than a human worker. 

Edge analytics  

Edge analytics gathers data from AI-driven devices and sensors, thus providing decentralized and quick statistics from those datasets. The data gathered by Edge, after analysis, can be converted into statistics which then can be used to optimize operations. Edge analytics can also be used to: 

  • Monitor the health and safety of employees 
  • Enhance production quality and outcome  
  • Identify the early stages of performance impairment and risk failure. 


No other technology is as efficient as robotics to automate business operations, reduce workload, increase production, and minimize errors. Robots reduce the workload of employees and allow them to spend more time on much more complex tasks. With AI being added to robotics, they not only monitor their actions but also train themselves to work more efficiently and improve their performances every day.  

Quality checks 

This is one of the most important use cases of artificial intelligence in the manufacturing sector. Sometimes detecting the internal malfunctions of equipment becomes difficult to even though experts are not able to monitor the performance of the products and find out their shortcomings. But this task can be easily completed with the help of Artificial Intelligence (AI) and Machine Learning (ML) as AI tools and applications can effectively detect minor bugs in machinery.  Therefore, it is clear that artificial intelligence ensures quality checks and control in manufacturing. Smart AI solutions track machine productivity and performance, enhance productivity, detect faults, and eliminate costs of maintenance as well.  

Customer management  

Smart AI chatbots not only help in increasing sales, productivity, and performance but also helps in improving customer service. This reduces the waiting time of customer, gives instant replies to them, build customer relation using CRM tools, and use consumer data to make informed decisions.  

Wrap Up 

AI and industrial automation have achieved extensive and significant progress in recent years. The development of machine learning techniques, the most sophisticated robotic technology, the increase in the growth of computer power, and ultra-modern sensors – all have contributed to the emergence of AI as a superpower in the modern era. The deep learning and speech recognition skills enabled AI to collect and extract data, identify patterns, learn, and improve day-by-day. All these factors made AI highly adaptable to the human environment. The implementation of Artificial Intelligence (AI) in the manufacturing industry not only supports the manufacturers but also assists technology developers in improving the quality of their ideas, bringing out ‘outside the box’ digital technology solutions, and building them into reality with expert assistance. 

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Use Cases of Blockchain Technology

Blockchain is known to play a major role in cryptocurrency systems like Bitcoin to maintain safe and decentralized transaction records. Bitcoin is a major reason why blockchain has gained popularity in the last decade. It has emerged as a game-changing terminology for various industries including supply chain, health, education, IoT, and many more. The popularity and industrial applications of blockchain are booming because of its inherent advantages – a ledger that is decentralized and distributed is unchangeable and fully transparent. A blockchain network does not require a centralized authority to handle it. The information saved in the blockchain ledger can be cross-checked by anyone in the network. The bottom line is safe, reliable, and offensive to attackers. 

Blockchain Use Cases 

Blockchain is a powerful technology that offers many advantages to various industries. Here are some of the major use cases of blockchain: 

Smart contracts  

Smart contracts are similar to normal agreements. The only difference is that the contract guidelines are implemented on a blockchain in real time. This not only eliminates the need for intermediaries but also increases the level of liability for all the parties involved, which is impossible in traditional contracts. This is also cost-effective, saves time, and ensures compliance from all the parties involved. With the discovery of benefits offered by contract-based blockchain in different sectors, there has been an increase in its popularity. Some of the examples include BurstlQ in healthcare and Propy in real estate. 

Internet of Things (IoT)  

The next reasonable rise in blockchain applications takes place in the Internet of Things (IoT). IoT has a lot of applications and numerous security concerns and the proliferation of IoT products gives the greatest opportunity for hackers to loot your data on everything from Google’s Nest to a smart thermostat. Such data breaches can be avoided using blockchain-infused IoT that offers a great extent of security with the transparency of technology and virtual integrity to maintain things “smart”. Filament, HYPR, and XAGE security are some of the US companies that use blockchain in IoT to make it smarter and more secure. 

Personal identity security  

As per the reports, around 14.4 million Americans have been subjected to identity theft in a year, which indicates that 1 out of every 15 people is becoming the victim of identity fraud. This type of fraud can range from the forgery of documents to personal file hacking. The government can see a significant reduction in identity theft claims by storing sensitive data like social security numbers, certificates of birth, PAN, etc. in a decentralized and fixed blockchain ledger. Some blockchain-based initiatives at the forefront of identity security include Ligero, Illinois blockchain initiative, and Civic. 


Blockchain in health care has played a significant role in managing patient data and security. With a huge number of data breaches and leaks, protecting personal data has become a serious concern for patients and hospitals. The transparency blockchain has offered gave assurance that changes in the information are clearly visible and cannot be meddled with. This way, blockchain technology has enabled the quick and secure provision of sensitive healthcare information to providers and patients, while giving privacy and transparency through encryption and sophisticated security codes. It also makes patient-provider communication efficient because of its decentralized nature. Since information is stored in multiple nodes, any change made in them will be updated in every node connected, thus helping to deliver updated information to doctors as well as patients.  


The use of blockchain in logistics systems and distribution networks allows easy tracking of goods. Though companies try to invest in tracking technology, the safety of the technology remains questionable as they lack a secure authentication process. This may give way to cyber criminals making use of network information. Blockchain guarantees you an appropriate authentication channel with verification. Plus, it facilitates live tracking of all your deliveries, thus ensuring a better customer experience. Moreover, it is also helpful in improving the national as well as international delivery processes while increasing their cargo capacity and their processing efficiency. If used properly, it will withstand a lot of adverse conditions that provide better transparency in logistics. This also builds reliability and trust between companies. With better transparency, invoice disputes will be reduced, labor exploitation will be reduced, and better auditing costs will be incurred. 


We have already mentioned the use of blockchain ledgers to secure crucial and sensitive data. But blockchains in the government sector have other uses as well. As reported by New York Times, blockchain can play a major role in enhancing the functional efficiency of bureaucracy, and accountability, and reducing large financial liabilities. Blockchain is also capable of cutting down millions of hours of red tape each year, using smart contracts to keep public officials responsible, and recording all activities to provide transparency. Moreover, blockchain-based voting can enhance the engagement of the public in elections as it provides the security and scrupulousness that enables voting from mobile devices. Voatz, the State of Delaware, and Follow My Vote are some of the companies and government agencies that use blockchain applications to improve government. 


The majority of prevailing issues in the media are related to intellectual property rights, data privacy, and royalty payments. As per the study conducted by Deloitte, media digitization is the triggering factor behind the widespread sharing of copyright-infringing content. According to Deloitte, blockchain can offer the media industry the facelift it needs in terms of copyrights, payments, and piracy. The power of blockchain in the media industry lies in its ability to block the presence of digital assets in different locations, such as mp3 files. It facilitates sharing the file while maintaining ownership as it is not possible to make piracy virtually with a transparent ledger system. In addition, blockchain enables advertisement agencies to aim at the target audience and musicians to gain appropriate royalty, thus maintaining data integrity. The companies that utilize blockchain in the media industry are MadHive, Steem, and Open Music initiative. 

Money Transfer  

The two best technology use cases of blockchain are money transfer and payment processing. Bitcoin-initiated, cryptocurrency transfer applications have gained much popularity in recent years. Blockchain technology enables transactions at a greater speech in real-time, thus saving time and money as it does not charge any transaction fee like most banks or financial institutions. It also supports faster and more secure cross-border payments (payments made between people in different countries), enables verify transactions that offer extra security, avoids any kind of intermediaries or third person between the payee and the payer, etc.  

Wrap Up 

Here we have discussed some major sectors that utilize blockchain in different ways. It has been proven that the advantages or uses of blockchain applications are not limited to cryptocurrency and bitcoin. The transparency and reliability it offers have impressed a lot of businesses that saved time and resources using blockchain technology. It is clear that blockchain is going to dominate the economy at some point in the future. 

Artificial Intelligence in Logistics 

Companies that develop artificial intelligence solutions is something that is gaining more attention from businesses and organizations these days. AI has emerged as a huge trend in many industries. The logistics industry is one where artificial intelligence can serve many of its purposes. Transportation, delivery, and product supply are also part of logistics where AI makes a difference.  

AI applications in logistics 

Though there are different ways of implementing artificial intelligence in the logistics industry, there are some major AI applications or use cases which are explained below: 


Planning for a logistics industry has many aspects. Effective and feasible approaches to planning are very much necessary for coherent coordination and proper use of resources, both financially and humanely. The use of live data along with the smart solutions of artificial intelligence facilitates more precise forecasting optimization and has only a few errors when compared to conventional prediction methods. This enables the manufacturers to manage human resources and other resources in the most efficient and cost-effective way, thus minimizing the holding cost which can lead to costly production. 

Automated warehousing  

An automated warehousing system is a relevant area where we can employ artificial intelligence. Many businesses are relying on warehouse robots to enhance the management of the supply chain in businesses. The value of warehouse robots with the capabilities of AI in the market was $2.28 billion as per the records of 2016 and is expected to show a growth rate of 11.8%. Since they are driven by artificial intelligence technology, performing tasks like arranging, packaging, and transporting goods and services becomes easy and effortless. Amazon Robotics is one of the best examples of warehouse robots.  

Autonomous devices and services  

Modern artificial intelligence technology is driving most of the autonomous vehicle systems such as drones, self-driving cars, robots, etc., that offer a helping hand in labor-intensive works at logistics and factories. Artificial intelligence is ideal for these tasks as it does not need analytical skills. Moreover, self-driving cars can significantly reduce the reliance on human drivers. Such technologies aim at supporting the health and safety of the driver by minimizing fuel consumption and excessive emission of carbon from vehicles.  


The computer vision technology enabled in AI helps in detecting malfunctions by identifying the type and intensity of the damage. This allows the manufacturer to avoid or minimize any further damage and work on the same as it analyzes the live data gathered from sensors installed in the machines. Machine Learning (ML) technology enables analytics tools that improve forecast analysis, and technicians to recognize patterns from sensor data. Hence, allows the manufacturers to take appropriate actions to make the situation better before something goes wrong with the machinery. 

Back-end operations  

Automation is not just limited to heavy tasks that require a great amount of human effort. It can lift the weight of many silly and repetitive tasks from the worker’s shoulder. AI also contributes to improving the automation of unskilled and hand-operated office work such as billing of products and services, generating reports, email processing, organizing and tracking transportation of goods, employee management, etc. Artificial Intelligence can also improve sales and marketing departments by generating more accurate market analytics, lead scoring, and email marketing. 

Conversational agents   

Customer service is another important area of logistics on the verge of automation. As AI-driven conversational bots can acquire a significant position in customer service, it is time for the logistics industry to go beyond conventional voice-based picking. Artificial intelligence boosts the use of advanced conversational solutions throughout the logistic platforms from regular consumer interactive platforms to warehouse interactions. Moreover, new innovations in NLP (natural language processing) algorithms expand the scope of conversation AI through: 

  • Feed in, store, and extract product information. 
  • Activate an automated picking system in voice-input-assisted warehouses. 
  • Use voice input to guide drivers. 

Sales and marketing optimizations  

Many giant automobile companies such as Tesla, Benz, etc., are investing a good share in automated vehicles or autonomous vehicles. As per BCG figures, 25% of the miles covered by private vehicles will be replaced by shared autonomous electric vehicles (SAEVs) in 2030. Drones are also getting much popularity in logistics industries as they are considered a reliable, and safe option for the delivery of various products to different locations, especially products with less shelf life. These delivery drones play a major role not just in eliminating unnecessary costs like delivery charges but also in freight management. 

Better demand prediction 

It is essential for the successful running of a company to predict the number of goods and supplies required for the future. Any failure to make accurate forecasts and inability to meet the demand because of the low quantity of goods results in financial loss, which is the last thing any company would want to face. When the predictions are not accurate, you will lose inventory and sales. AI algorithms are very good at predicting these trends. In fact, they can predict far better than human specialists, after analyzing the previous transaction history.  

Wrap Up 

With digitization hitting almost all global logistics companies, steps are being taken to disrupt the Artificial Intelligence (AI) industry. In order to remain competitive and dynamic, international logistics giants like DHL are taking advantage of AI’s machine learning technologies and Robotic Process Automation to efficiently automate crucial business processes. After considering all advantages and benefits it could offer, incorporating Artificial Intelligence technology into the logistics industry is very important to outperform rivals and to bring in a reasonable and positive revolution in the current business world.  

Artificial intelligence not only drives the process faster but also gives emphasis to precision and workload reduction in the industry. There are many applications of AI that have proved to be cost-effective, efficient, and useful at the same time. It enables the use of human resources in tasks in addition to people who do not need skills. All these qualities make AI the best option for the logistics industry.  

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Artificial Intelligence in Cyber Security 

Conventional security measures in the cyber world depend on antivirus programming such as firewalls. There are many tools to identify and prevent security vulnerabilities in cyberspace. Even with the advancement of technology, cybercrime continues to grow and become more complex every day. Cybercriminals carry out innovative attacks that endanger modern security systems. Companies opt for Artificial Intelligence (AI) to improve the capability of their system to identify vulnerabilities and cope with rising cyber-attacks. Artificial intelligence in cyber security helps companies not only to protect their defense systems but also to help them in analyzing crimes. Meanwhile, AI utilizes machine learning (ML), deep learning, natural language processing (NLP), etc. in order to make it difficult for cybercriminals to access servers, as well as other relevant data stored inside the system. 

Impact of Artificial Intelligence on Cyber Security 

The application of artificial intelligence has a great impact on cyberspace, especially when it comes to matters that concern the security of a system or network. Other than offering aid to experts in identifying cyber threats or attacks, artificial intelligence has the following impacts: 

Phishing Detection and Prevention Control  

Phishing is one of the most extensively used fraudulent activities in which attackers send emails pretending to be from reputed firms in order to lure individuals and companies to obtain crucial information. One out of 99 messages are phishing messages and that makes a phishing attack a common one. Fortunately, AI-ML may have a significant role to play in preventing and diverting phishing attacks. Computer-based Machine Learning can identify and track over 10,000 dynamic phishing resources and enables us to respond to and solve them faster than people can. In addition, AI-ML works in coordination to percolate phishing attacks from around the world. AI has also made it possible to distinguish between a fake site and a real site quickly.  

Secure Authentication  

Passwords are always extraordinarily sensitive when it comes to security. In fact, they are borderline between cyber attackers and our data/records. AI uses various components to recognize an individual using physically identifiable biometrics like facial features, fingerprint, and voice recognition that acts as the primary means of safe and secure verification. For instance, a cell phone verifies the unique finger impression and facial features to allow you to sign in. This process consists of a program that checks basic information, focusing on your face and fingers to identify if a login is valid. In addition, AI can look for various other elements to determine if any client is authorized to sign in to any specific device. The technology analyzes factors such as your voice, how you enter keys, typing error rate, and composing speed.  

Quick Detection and Response Time  

Artificial intelligence can cross-reference cautions and security information sources instantaneously in order to speed up the detection of authentic issues. This even helps the experts in digital security to approach each security issue that needs to be taken care of. Anyhow, it has the backing of AI frameworks that continuously suggest plans to improve responses. 

Better Endpoint Protection 

With the increase in the number of remotely operating devices, AI plays a major role in protecting all endpoints. Though malware and ransomware attacks can be prevented using antivirus solutions and VPNs, they operate on the basis of signatures. This indicates that signature definitions need to be updated in order to remain safe from newly emerging threats. If a new type of malware attack occurs, antivirus solutions and VPNs may not be able to protect against it. But in the case of AI-driven endpoint protection, if something unusual happens, the AI points it out and takes necessary action by either notifying the technician or returning it to a secure condition after a ransomware attack. Instead of waiting for signature updates, it gives active protection against threats. 

No Zero-day Vulnerabilities  

Any new threat a security expert has to deal with and does not have a patch or remedy to fix is considered a zero-day vulnerability. Zero-Day indicates that the experts have zero days to solve the problem, and an attacker may have already taken advantage of them. Mostly these threats are seen in IoT devices that are not secured. AI’s machine learning algorithms analyze inaccuracies in network traffic, detect zero-day threats and eliminate security vulnerabilities and prevent patch exploitation. 

Automating Security Tasks  

Machine Learning (ML) and Robotic Process Automation (RPA) play an important role in reducing monotonous and time-consuming security tasks, which in turn help human workers to focus more on other relevant tasks that require human attention intelligence. AI algorithms monitor the functioning of the system throughout and are capable of identifying malicious activities or threats like ransomware. Once the threat is identified, it destroys the threat and prevents it before it starts attacking the system. Machine learning can also be used to automate repetitive tasks such as checking network traffic, eliminating viruses, and analyzing network logs. 

Behavioral Analytics  

One of the important uses of AI in the cyber security department is its capability and efficiency in analyzing behavioral patterns. This indicates that ML calculations can study and exemplify human behavior by distinguishing the way humans use gadgets and online platforms. AI calculations watch any kind of unusual activities or any conduct that is different from your standard examples. It can identify it as performed by a suspicious client. AI calculations can perform various exercises ranging from huge online buys transported to addresses different from yours, an abrupt spike in report downloads from your documented envelopes, or an unforeseen variation in your composing speed. 

Preventing Online Frauds  

Organizations should have the option to detect a digital attack in advance. This is an option to thwart what the enemy is trying to do. Artificial Intelligence has proven to be of amazing value in identifying cyber-attacks and identifying risks before the threats take advantage of the vulnerabilities in your data frameworks. When it comes to fraud prevention AI redefines itself by taking past experience into account and in light of new behaviors, and trends in transaction irregularities. AI provides fraud analysts with real-time risk scores and more information about threshold score settings to increase sales and reduce fraud losses. 

Wrap up 

The application of Artificial Intelligence (AI) in cyber security will emerge as the basic mechanism for developing and controlling security systems. Cyber security professionals will get more response time to combat these malicious attacks because AI can detect many threats before they create damage. AI can also detect cyber security vulnerabilities and give recommendations to specialists in order to take appropriate action.  

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Artificial Intelligence in Fintech 

With technological advancements, FinTech companies (Financial Technology) have witnessed revolutionary changes during the last two decades. AI is on track to become a key player in financial services in the short term. Banks and financial institutions are implementing AI solutions to utilize information and statistics stored in unstructured documents and to automate and speed up conventional banking processes at a faster pace. AI and machine learning have always been a great advantage to banks and FinTech because they can process large amounts of customer data, which are then used to gain insights about the preferences of choices of goods and services the customers would want. This helps in building effective customer relationships. 

Artificial Intelligence in Fintech 

Here are some of the major applications of artificial intelligence in fintech that show what makes AI so relevant in financial technology. 

Digital finance advisor  

Automated or digital assistants are used in finance to provide advisory services to customers and to assist customers in making financial decisions. AI can transform fintech with Robo and Bionic advisory often referred to as “robot advisors”, that allow customers to track changes in price trends, and events, and manage their expenses and savings. Robo is a part of financial services that provide relevant advice or assistance in e-investment management without human intervention. Meanwhile, Bionic Advisory offers customized services to companies and organizations in the financial world and promises in-depth investment research and methods to use cutting-edge investment technology. This way such digital finance assistants help users to make financial decisions based on suggestions given by the assistant. Transactional bots are the best example of automated financial advisors because the use of transactional bots has grown rapidly and has become one of the most popular use cases of AI in all industries. 

Insurance management  

Implementation of AI in fintech helps in automating the process of insurance management and giving more precise or relevant data. Automated chatbots in the insurance sector can accelerate the process and help customers in pointing out the insurance requirements by linking multiple data sets. Additionally, this technology also explains levels of risk depending on user activity. Thus, the application of artificial intelligence in Insurance Management helps in identifying any type of risks or problems. It also helps to minimize functional costs and drive the process of decision-making faster.  

Customer support  

Every business and financial institution gives special attention to customer experience and providing services online is one way of making the process smooth and efficient. The application of AI allows you to have direct communication with the customers and to gain real-time access to in-depth customer information within no time. Financial institutions can make use of AI-driven chatbots to provide customers with information about their transaction history, current balance, and other related data. This way AI not only reduces costs but also improves customer satisfaction as well. Moreover, AI-driven automated chatbots can deliver a more positive or interactive communication experience with customers than ‘artificial’ conversations. 

Asset management  

Investment funds leaned on intricate algorithms for a long time to generate predictions and simulations. Due to this, the asset and wealth management industry has to reorganize a number of processes and provide pristine services like wealth management tools. Fintech companies paid attention to this and are integrating these solutions within their applications so that customers can take advantage of them. They started using AI algorithms to forecast future Fintech trends. With AI algorithms, fund managers became capable of successfully monitoring the market, which is a breakthrough development. AI not only automates the process but also helps to perform intelligent operations. App users can now access their bank statements and make critical transactions from any device, which in turn eliminated the need for intermediaries.  


Disruptive technologies like Artificial Intelligence and Machine Learning (ML) are capable of processing a large volume of data and translating it into simple results in their own way. They also can do the work at a low cost while maintaining the quality of the data, which in turn allows the customers to monitor their expenses and gain personalized financial insights. Data extraction and data mining can be used by companies to detect patterns and forecast the future using this big data and with proper assistance from analysts. Finally, it can lead us to predict the trends and choices of customers during the purchase. 


Insurance companies offer underwriting services mainly for loans and investments. An AI-driven model can assess or evaluate the credit risk of a client in real-time, thus enabling advisors to build the best deal possible. The application of Artificial Intelligence (AI) in underwriting services enhances the efficacy of proposals and customer experience by fast-tracking the process and time taken to perform such operations. 

Security and fraud detection  

The fintech industry needs a lot of security to protect crucial data and other information from fraud. Studies have proven that financial institutions are highly vulnerable to cybercrimes and fraud.  AI algorithms are capable of detecting suspicious behavior and informing customers about any unusual activity that takes place in the network. As these technologies can constantly monitor abnormal patterns 24/7, there is no need to stay alert all the time. This helps companies to keep an eye on what is happening behind their backs while being calm that their assets are safe. AI algorithms can also detect other illegal activities such as money laundering.  

Wrap Up 

Artificial Intelligence (AI) solutions in FinTech has the potential to assist businesses in attaining their development goals, achieving competitive advantages, minimizing operating expenses, making internal processes more efficient, and becoming the best choice for their customers. It also benefits users to handle their personal finances. Moreover, artificial intelligence enables the creation of precise statistics related to customers and clients. Eventually, this reduces the time it takes to analyze a large volume of data. AI in Fintech is designed and programmed to manage day-to-day financial services. We have also discussed many other applications of AI in fintech which have proven to survive the times in many conditions. 

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