Author: Mubarak Musthafa

VP of Technology & Services at ClaySys Technologies

Artificial Intelligence in Banking 

Posted on by Mubarak Musthafa

Industries are going behind new methods to adapt to changing preferences of the customer and to cope with the pace of emerging needs and demands. The banking sector is going through digitalization at a much faster pace. The flow of the operational process is clearly integrated with Artificial Intelligence, Machine learning, and Robotic Process Automation solutions. The end result of incorporating these technologies brings significant gains because AI is capable of improving processes virtually in all areas of banking. From back-end automation to front-end operations, AI offers significant benefits to the banks that implement it. AI includes a range of technologies, from fully machine-powered or autonomous intelligence to aided or enhanced intelligence. Some relatively simple AI tools such as Intelligent Process Automation (IPA) have already been implemented in banks.  

Uses of Artificial Intelligence in Banking 

Here are some of the major use cases or applications of artificial intelligence in the banking sector. 

Fraud detection and cyber security  

Fraud detection is one of the most relevant functions of artificial intelligence. Every day, a large number of digital transactions take place, with customers paying bills, withdrawing money, and depositing checks, and there is another bundle of transactions happening through apps or websites. Therefore, there is a growing need for the banking sector to increase its cyber security and efficiency to detect fraud. Since banking facilities have become more accessible with advancements in technology, it has also increased the chance of fraudulent activities occurring in these services.  

The machine learning capabilities of AI enable easy detection of fraud more accurately than any expert professional, thus offering security in online banking services. AI can track any loophole or unusual activity in the system and notify customers and banks to eliminate risk or damage associated with it. The monitoring capabilities of Artificial Intelligence also enable banks to deal with cyber-attacks in the banking sector before it reaches the internal system, employees, or customers. 

Chatbots  

Maintaining an effective customer relationship is very important for many businesses and the banking sector is not an exclusion. It will not be easy to deal with customers, answering every single query of them, especially when there are more important tasks to be done. But with a chatbot, all these things can be managed easily as it significantly eliminates the need to maintain and operate call centers. Chatbots, once deployed, will be able to work 24 * 7 as they will not get tired or take recess as human employees do. These bots easily understand the needs of a user efficiently and provide improved customer support. Chatbots learn from customer interactions and train themselves to improve their efficiency. Integrating chatbots with banking services ensures that customers can reach out to the bank’s support at any time of the day. Moreover, chatbots deliver personalized support to each customer after analyzing their behavior and suggesting the best financial services according to their requirements. 

Track marketing trends  

A large volume of data is always a part of banking institutions and it is artificial intelligence that helps banks to process such a huge volume of data AI also makes predictions regarding emerging trends, currencies, and stocks. The AI-based machine learning technology in banking helps to assess market sentiment, recommend options to make investments, and suggest the best time for investing in stocks. and warns if it identifies any potential risk. Because of the great data processing capacity of AI technology, it helps to accelerate decision-making and facilitate smooth business for banks and their clients. 

Loan and credit support  

Banks have begun to integrate Artificial Intelligence solutions to make loan and credit decisions more secure, informed, and profitable. Many banks still take account of credit history, credit score, and references in determining the creditworthiness of a person or an organization. However, sometimes these credit reporting systems are delivering inaccurate and riddled data with errors. There are chances for loss of original transaction history and misrepresentation of creditors. AI-based loan and credit systems decide the creditworthiness of customers by analyzing the behavior and pattern of their credit history. In addition, the system sends alerts to banks about certain customer behaviors that show the likelihood of default. 

Risk management  

The banking sector is often affected by many external global factors like natural disasters, political disparities between countries, fluctuations in exchange rates of currencies, etc. Making crucial business decisions requires extra cautiousness, especially during such complex and unpredictable situations. AI-based analytics will help you to get a clear idea about things that are yet to come, to remain prepared, and to make wise decisions. AI identifies risky applications by assessing the likeliness of a client failing to repay a loan. This kind of prediction is made by analyzing previous behavior patterns and data in the client’s smartphone.  

Regulatory compliance  

If we list the most regulated sectors of the world economy, the banking sector would be holding the top place. Governments have introduced a certain kind of regulatory control over banking institutions to make sure that customers do not take advantage of banks to commit financial crimes. Such compliances also ensure that banks maintain acceptable risk profiles to rule out large-scale defaults. Most banks have comprised an internal compliance team that handles such issues, but these processes are more time-consuming and need large investments when it is performed manually.  

The compliance regulations on banking institutions are subject to change from time to time, and banks are required to comply with these regulations and update their processes and workflows regularly. It is true that AI Banking cannot take up the role of a compliance analyst but it will assist them in making operations faster and more efficient.  

Wrap Up 

Every business is going through a digital revolution that is affecting the functionalities of many sectors in different ways. Just like any other sector that focuses on embracing the revolution to gain profits, banking is also in a position. the uses or applications we have discussed here give a clear picture of what artificial intelligence has in store for banking. Though it mainly aims at improving customer relationships and automating complex processes, AI never fails to attend to the challenges faced by banking institutions due to digitalization. 

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Artificial Intelligence in Finance 

Posted on by Mubarak Musthafa

Artificial Intelligence (AI) in finance indicates the use of financial software developed by an artificial intelligence company using machine learning algorithms that enable financial institutions to automate day-to-day operations. New approaches have been taken up to optimize AI business operations in the financial sector and avoid security issues, thereby increasing the confidence of customers in financial institutions. The application of artificial intelligence in the financial sector has provided customers with a safe and smooth way to save, spend, and handle their money.  

Financial professionals and clients are more likely to have frequent AI encounters, as most common service-related problems are managed or solved to some extent through automation powered by artificial intelligence solutions. The chances for an acceleration in this trend are high as it has to meet the growing customer demand to offer a quick, convenient, and safer financial experience. 

Applications of AI in Finance 

There are many ways through which artificial intelligence is leveraged with finance or financial institutions. Let us talk about some of the challenges addressed by AI when implemented properly in the financial sector. Below are some of the main use cases or major challenges addressed by artificial intelligence in the financial sector.  

Risk management  

Emphasis is placed on appropriate risk assessment in the financial sector as risks can be devastating to any financial institution. Prompt and accurate predictions related to possible threats are the crucial factor responsible for maintaining the safety of financial institutions. Human employees can make mistakes as they may not pay attention to potential risks that might cost the business a lot. In such cases, the chances of a machine learning model going wrong are very low as they are constantly learning from a large dataset to make precise decisions.  

In addition to helping with decision-making, these models aid in automating the whole process, thus saving time. Considering anomalous data while evaluating and managing risks is very challenging for human employees. AI’s ability to navigate through huge volumes of unstructured and structured data makes it possible to make precise and prompt predictions about potential risks. It enables financial institutions to actively work in order to eliminate the possible losses that come with the risks. 

Fraud Prevention  

With the world transforming from liquid cash to digital money for almost every activity ranging from shopping to bill payments using a smartphone, financial institutions have become more vulnerable to fraudulent activities. The digitization of such financial activities has resulted in the emergence of cybercrimes including security attacks. These institutions have specialized departments for detecting and preventing fraud and they also invest huge resources in such departments. Any negligence cannot be welcomed there when all activities are closely monitored to ensure transparency. 

Artificial intelligence plays a crucial role in detecting strange behaviors, thereby ensuring the safety of transactions conducted online. This is because the AI finance system analyzes the scale of previous transactions and alerts the system in case of any unusual transactions. For instance, when a user uses a card from one geographical location and a strange amount is withdrawn using the same card from another location within a few minutes, it is identified as unusual activity. Such suspicious transactions are noted because of the previous history of the user that reveals he/she can’t reach a geographical location in a matter of minutes. As AI finance systems are constantly learning from each activity of the user it is possible for them to recognize an activity even if a normal and authentic transaction is taking place. 

Digitizing paper records  

There is a huge volume of historical data in financial institutions, especially in the form of paper documents. Since the records are prone to damage or losses and are chaotic in nature, they have been stored in drawers, shelves, cabinets, or in other storage spaces. Manual digitization of such paper records is a challenging task, especially if the organization has been in business for a long period of time. The combination of all these components requires a step to digitize all paper documents for ease of use. 

 AI always relies on historical or previous data during training in order to make effective decisions. Artificial intelligence also helps in digitizing these paper documents rather than entering the data manually, thus enabling the employees to scan and upload the document to the software. These AI-driven software packages can obtain every piece of information from the document as they are trained to identify the characters in the document. OCR (Optical Character Recognition) and ICR (Intelligent Character Recognition) are 2 such A-driven technologies. 

Automated Financial advisory  

As the financial industry is leveraged with Artificial Intelligence, we are witnessing the proliferation of Robo-advisors. These Robo-advisors use AI and machine learning algorithms to deliver financial advice to clients with least or no human intervention. The robot advisor uses an online survey to collect data in order to analyze the financial status and future goals of the clients. This data is later used to provide financial advice to clients. In addition, this Robo-advisor helps clients save their time and money as it delivers precise and data-driven advice. It also allows the integration of human intelligence with AI to provide the best financial advice with future insights. 

Insurance claim automation  

AI chatbots and AI-driven software facilitate the automation of the insurance claim process and minimize the risk associated with overpayment. There is an internal mechanism within the insurance companies to process these claims, but this may take a long time as there are many people claiming their insurance. In addition to this sluggish insurance claim process, too many claims can make it difficult for insurance companies to identify important patterns in claim data. 

AI-induced systems can easily manage the claim process by minimizing the amount of time it takes to identify patterns and process each claim. Many organizations have started developing software and chatbots for insurance companies that utilize the features of AI and predictive analysis to minimize the occurrence of overpayments. Moreover, AI-driven software can identify inaccuracies in an insurance company’s payment behavior based on previous data. 

Wrap Up 

Modern finance has started implementing Artificial Intelligence for diverse uses. Though the influence of AI has universally affected many sectors and industries, the financial industry is considered to be one of the earliest sectors to embrace this amazing technology. It is the best example of one of the most benefitted or profited sectors because of AI. Therefore, many large financial institutions are investing a good share of their resources in AI due to the undeniable benefits it offers. Since the financial industry is one of the core industries that many businesses rely on, this prompts many organizations and industries to take up artificial intelligence for their benefit.  

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Use of Artificial Intelligence in Marketing 

Posted on by Mubarak Musthafa

Artificial intelligence in marketing refers to the use of consumer data from online as well as offline sources to predict different consumer behaviors while using applications or websites. This helps businesses to distribute specific content to their target audience through the proper channel at the right time. Digital marketers can use big data statistics to improve the performance and ROI of their ventures while not putting too much effort on their part. Though this description gives an overview of Artificial Intelligence development in marketing, there is a lot more to explain. The application of AI also helps to make campaigns more effective and to achieve their goals by analyzing prevailing data from previous campaigns to give recommendations for tuning campaigns depending on different characteristics. 

Benefits of AI in marketing 

Given the benefits of AI in marketing, many brands have embraced it as a way to not only improve the shopping experience for consumers but also enhance their brand recall. Let us consider the above scenario and explain what the marketing department has been able to reap from using AI.  

Predicting consumer behavior  

Predicting customer behavior would be a powerful solution for many businesses as no business is likely to survive without customers. Knowing how the products have been used by your customers is very important in measuring the success of your business, but it would be better if you could figure out where things are going based on the little trends, we might not be able to identify from the ground. Here is where artificial intelligence comes up with an aid to show how our customers actually behave. AI helps businesses to push away the boundaries of possibilities in online retail. On the other hand, AI analyzes and predicts the leaving behavior of customers from a specific market or business, thus helping companies to focus on the causes of deviation and find a solution at the beginning itself. 

Improved customer experience  

With AI-driven chatbots and virtual trials, AI technology has taken the shopping experience of customers to a greater level. Virtual trials permit customers to check the product they want to purchase and to have an idea about what or how they look like. This is an opportunity for the customers to get detailed insights about a product, thereby eliminating the need for an exchange or return and providing satisfaction with the product. Meanwhile, chatbots help bring customers close to the brand by interacting with them and giving responses to their questions and queries. The more you satisfy your customers by engaging them in your business, the more they invest their interest in your brand. 

Minimized load times  

There must have been a situation at least once in your life where you couldn’t buy a product you were looking for, just because the website took a long time to open up or respond. Customers usually don’t have the patience to purchase a product from a site that takes a long time to load. This definitely affects the sales of products on such websites. AI-based algorithms can easily determine the chances for a user to click on a particular link or product and decrease the loading time of the page in advance. This way the user is driven faster to the product they are looking for, which in turn increases sales as well.  Hence an in-depth understanding of the customer’s behavior and loading their preferred content leads to a significant reduction in load time. 

Content creation  

Companies are beginning to extract benefits from AI as it helps them develop content for the promotion of their brands. Artificial Intelligence was mainly employed in businesses to create reports about the performance and marketing data of organizations. But today, artificial intelligence can create blog posts for companies that increase traffic to their site using SEO strategies to boot.  

Track marketing trends  

Market trends keep changing within a regular period of time, mainly because of the changing customer needs and demands. It is not that difficult to track the trend but coping with the trend and analyzing the reaction of the audience to a trend is quite difficult. Here, Artificial Intelligence allows you not only to track the market trends but also to understand the reaction of the audience to the brand. Hence, you can use those insights in your marketing strategy and increase the reach of the product. Remaining at the top of the market trend will benefit you with a group that wants to purchase your product in line with the trend. 

Ad and search engine optimization  

When AI is incorporated into your marketing plan, it not only helps make relevant corrections but also results in delivering good-quality content for your brand promotion. This is applicable to your ads and SEO. AI has proven the ability to analyze and finalize appropriate keywords for your brand which ensures that you are included in the recommendations of a broad audience than just a comparatively small target group. Taking into account the benefits of AI and the extent to which it is evolving, we know that it will survive here. At the same time, the way we use AI in our marketing plans is to maximize results with an increase in possibilities. 

Wrap Up 

In the initial days, the possibilities of artificial intelligence were confined due to inadequate computing powers. But today with the growth of technology, AI has become the next ‘ray of hope’ in the world of technology. In this digital world, everything is driven by data, especially with the support of AI and machine learning. Markets are no longer defined by boundaries, but by the perspective of a company. The world itself has become a market with a lot of possibilities and competition. Artificial intelligence can be just a new addition to the current modern world but it will continue to exist and evolve over time with the increased adoption of technology in different areas.  

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7 Real-World Applications of Deep Learning

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What Is Deep Learning?

Deep learning belongs to the extensive family of machine learning methods that enable computer systems to display the same behavior as humans during specific situations, thus promoting complete automation by eliminating human intervention in many activities.  

Deep learning allows computer models to understand and do categorization tasks directly from pictures, text, or sound. Deep learning models can exceed the performance of humans and can acquire cutting-edge accuracy in many operations. Being a booming industry with numerous possibilities, deep learning continues to power up businesses as well as client satisfaction. Many technological innovations like self-driving cars, virtual assistants (Alexa, Siri), etc. wouldn’t be here today without deep learning.  

Deep Learning Applications 

Deep learning is a machine learning technique that can be applied in many areas to create huge changes in the field of technology. Here, let’s take a look at some of the major deep-learning applications:  

Self–Driving Cars  

Deep learning is a technique that breathes life into self-driving cars. Millions of data sets are integrated into a system to develop a model, upskill a machine, and then check the outcome in a safe environment. Usually, it follows a routine cycle of testing and application of deep learning algorithms and exposure to billions of different situations in order to ensure safe driving. Self-driven cars make use of the data obtained from cameras, sensors, and geo-mapping to develop concise and complex models for navigating traffic and identifying lanes, signages, pedestrian-only paths, live components such as roadblocks, and volume of traffic. 

Fraud Detection  

In this era where everything is digitized, financial and banking institutions have no other ways but to go with the flow. Since we have begun to witness a digital transformation in the banking and financial sectors, cyber threats to the banking sector have become a concern, which in turn demands the necessity of fraud detection. Deep learning comes to help this area in identifying transaction and credit score patterns. Deep learning uses fraud detection algorithms and throws light into areas where unusual behavior happens, thus detecting fraud and preventing it. Additionally, deep learning can be used in news aggregation to strengthen efforts to deliver customized news to readers. With the internet being the source of all types of news, whether it is fake or real, it is very difficult to detect fake news as it gets copied across the channels automatically, by the bots. Deep Learning enables you to create classifiers that identify fake or biased news, delete such news from your wall, and notify you about any potential privacy breach.  

Virtual Assistants  

With the help of deep learning technology, virtual assistants and chatbots grasp more knowledge about different topics like the preferences of the user, their favorite songs, places visited, etc. Virtual assistants and chatbots are automated to comprehend the commands given by the user and analyze natural human language. Virtual assistants are also capable of translating your conversation into text, booking appointments, making notes, etc. Amazon’s Alexa, Apple’s Siri, and Google Assistant are some of the most common examples of deep learning applications in virtual assistants. Whenever a user interacts with the assistant, it facilitates them the chance to comprehend your voice and pronunciation, thereby offering a secondary-level human interaction experience. 

Healthcare  

Deep learning in healthcare not only reduces costs but also helps in eliminating health risks. The application of deep learning in medicine has been proven to be valid in clinical research. Deep learning can be a helpful diagnostic companion for rehabilitation and gives alerts whenever there is a high-risk situation like respiratory failure or sepsis.  Precise and rapid diagnosis of critical diseases augmented clinicians attending patients in the absence of quality health care professionals, and the standardization of pathology results and course of treatments as well as predicting future health risks using genetics are a few of the deep learning projects moving faster in the healthcare domain. 

Entertainment  

Deep learning is the reason why most over-the-top (OTT) platforms are able to deliver customized content to the audience according to their preferences. It can even suggest particular shows or movies to a specific user, based on their previous watch history. Even VEVO has employed these deep learning techniques to build state-of-the-art data services not merely for offering customized experiences to its users but for companies, artists, and other business groups to create statistic-based performance and fame. Content editing, transcriptions, audio-video syncing, tagging, etc. are some other major applications of deep learning in the entertainment sector. 

Visual Recognition  

Deep Learning enables sorting or categorizing images by identifying locations,  faces of people (even in a group picture), dates on which the photo is taken, occasions, etc. Searching and identifying a photo from a large dataset requires ultra-modern technology with visual recognition systems including multiple layers from standardized to advanced, to detect components.  

Natural Language Processing  

Teaching machines the entire structure of a language ranging from semantics to pronunciation is not a simple task. Even humans take years to be a thorough expert in a language they use every day. Natural language processing is the practice of training machines to learn human language and give proper responses in every situation, with the help of deep learning. Responding to questions, sorting texts, emotion analysis, and language modeling are all subdivisions of natural language processing that accelerate the use of deep learning. 

Wrap Up 

Artificial intelligence development can help businesses reap tons of benefits when Deep Learning and AI are implemented in the right way. For those who are familiar with or proficient in deep learning, the opportunities, and possibilities they have are endless. Only those who are aware of the capabilities and impact of machine learning can understand how the application of deep learning works in different industrial sectors across the globe. Deep Learning continues to explore new unsolved areas, offering a solution to human problems all around the world. It is always a safer and better option even for governments to achieve success not just in administration but also in the military and other physical forces. Deep learning is not just limited to the above-explained areas it has a border area of application.  

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Artificial Intelligence in E-commerce

Posted on by Mubarak Musthafa

We are already aware of Artificial Intelligence (AI) as a cutting-edge technology, in which machines imitate human intelligence artificially. Things are becoming more interesting with artificial intelligence being implemented in different business sectors. It is not surprising that the e-commerce industry is using artificial intelligence not just for increasing sales but also to provide a better customer experience. Even e-commerce giants like Amazon have also adopted artificial intelligence applications in product recommendations and this resulted in an increase in Amazon’s overall revenue by 35 percent. Hereby, it is proved that the application of AI in the e-commerce industry has contributed to many benefits.  

AI applications in E-commerce 

The E-commerce industry is facing a great transformation with the use of artificial intelligence in online shopping, as it predicts the shopping pattern based on the customer’s purchase. Here are some major applications of artificial intelligence in e-commerce. 

Chatbots and virtual assistants 

Today there are no time restrictions or limitations in shopping as whatever we need can be ordered online and will be delivered to our doorsteps. We don’t have to wait in a long queue to get customer support. E-commerce platforms are providing 24/7 customer support and a hustle-free shopping experience with the help of digital assistants or chatbots. Since these support assistants are built using AI technologies, they are artificially trained to assist customers in the same way human agents do. Chatbots improve customer service. Other than customer support, they also offer other benefits, like: 

  • Comprehend voice-based commands from customers using Natural Language Processing (NLP). 
  • Address customer requirements through in-depth insights. 
  • Improves functionality through self-learning. 
  • Give customized or selected offers to consumers. 

Product recommendations  

One of the notable applications of AI in the e-commerce industry is personalized product recommendations to consumers. AI gives customized product recommendations to customers based on their previous search history, purchases, most browsed products, and browsing habits. As a result, the conversion rate has spiked up to 91.5% and the average order value to 3%. The benefits of product recommendations include: 

  • Increase in the number of customer returns. 
  • Increase in sales and customer retention. 
  • The customer gets a personalized shopping experience. 

Inventory management  

Effective inventory management involves the exact level of inventory maintenance that can meet market conditions without moving to unused stock. Unlike the conventional form of inventory management which was restricted to the prevailing stock levels, AI-induced inventory management enables you to keep track of data based on: 

  • Previous year’s sales trends. 
  • Expected or projected changes in the demand for products. 
  • Issues related to supply may affect the inventory level. 

Other than inventory management, AI also facilitates warehouse management where automated robots are being built to improve the future of the e-commerce industry. 

AI personalization  

When it comes to e-commerce marketing, personalization plays a major role and is rated as one of the most effective tools. Based on specific data collected from each user, Artificial Intelligence and Machine Learning in e-commerce gain important user statistics using this customer data. Boomtrain is an example of such an AI-enabled tool that is capable of analyzing customer data from different platforms like email campaigns or websites or mobile applications. These statistics help e-commerce retailers provide relevant product recommendations and a stable or uniform user experience throughout every device. 

Benefits of AI application in e-commerce 

As we have discussed the applications, here are some important benefits that accompany AI applications in e-commerce.  

Automation  

With the help of AI technology and algorithms, robots allow retailers to deliver to the customers whatever they want whenever they want something. With the expansion of companies and businesses all over the world, the e-commerce industry continues to grow with a large number of customers. This growth automatically increases the number of repetitive job launches of new products in multiple channels, providing discount offers to customers, etc. which in turn demands automation to be taken as a top priority feature that needs more investment in e-commerce stores.  

Retarget potential customers  

As per the reports of Conversica, the sales team fails to follow a minimum of 33% of marketing leads. This indicates that many of the potential customers interested in a product are left behind and due to excess unmanageable data, most teams are unable to perform relevant actions. This is where AI becomes a game-changer. Artificial intelligence helps in developing an impactive sales message that grabs the attention of your customers at the right time and designing solutions for your problems, thus increasing the sales cycle.   

Filter fake reviews  

Customers always go through the reviews before making a purchase. This is a common customer behavior through which customers try to analyze the value of the product they are planning to buy. Fake reviews are always a headache for online as well as e-commerce shops. It is found that negative reviews have influenced almost 86% of customers’ decisions. The possibilities of fake reviews put by competitors, bots, and paid review writers are very high. All of these affect customer purchases. Artificial intelligence is used by many e-commerce businesses to defend their businesses with more focus on verified reviews. One of the best examples is Amazon using artificial intelligence to counterfeit fake reviews and increase their 5-star ratings. They focus on important reviews of purchases made by verified customers and promote them.  

After-sales service  

Selling products and increasing sales alone cannot help your business grow. You need to extend your service even after the sale. This includes giving assistance in returning a product, replacements, and installations, answering customer queries, checking customer satisfaction, etc. All these procedures can be easily managed by artificial intelligence. AI identifies whenever the delivery of a product, sends feedback forms to customers, notifies them to fill in the form, and asks them to rate the purchase and review it to offer better services in the future. 

Wrap Up 

Artificial Intelligence has proven its capability in many other areas before, but implementing AI technology in retail or e-commerce can bring out the best outcomes for growth in the overall business revenue as well. As per the reports revealed by a Forbes article, more than half of marketers are planning to adopt AI technology in their business within a few years. 

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Types of Biometrics 

Posted on by Mubarak Musthafa

Introduction 

Biometrics refers to the measure of biological or behavioral characteristics that helps in identifying individuals. The biometric system, on the other hand, analyzes a person’s physical or behavioral traits and recognizes whether the individual is a genuine or fake user. Fingerprints and facial recognition are the most commonly known biometrics but there are other various types of biometrics used as security features to identify and authenticate people. Biometrics are designed in different forms and for different purposes including security, fraud prevention, gaining access, and many more. Moreover, there has been rapid development in the software used to gather biometric data.  

Types of biometrics 

As mentioned before, there are different kinds of biometrics that mainly fall under 2 categories: physiological biometrics and behavioral biometrics. Let us take a deep dive into them: 

Physiological Biometrics 

Physiological biometrics are taking an individual’s physical characteristics as an input to recognize identity. 

Fingerprints  

This is one of the most commonly used and oldest forms of biometrics and it uses the measurement of your unique finger ridges to identify an individual. Once the print is captured, the image is used to obtain a specific digital biometric template with the help of advanced algorithms. The template is analogized with existing or new scans to identify the match. 

Finger/hand veins  

The blood vessels under the skin of a human finger or hand have a unique pattern (formed by the veins and vessels that take blood to the heart) and this authentication technology works on the unique pattern formed by the veins and vessels. The infrared lights penetrate the skin, where they get imbibed into the deoxygenated blood and a specific cam device captures the digitized picture of the data and then saves or uses it to verify identity. All this makes it harder to hack this scan. 

Iris recognition  

The color and pattern of the iris vary in individuals. The pattern of the iris and color is different for each individual. It is the colored part of the eye with a circular opening circular in the center. The muscles that shape the pupil of the eye authorize the passage of light into the eye. The identity of an individual can be verified accurately by the biometric devices as they take measurements of the unique muscle folds in the eye.  

Facial recognition  

Another commonly used and oldest form of biometrics. This technology requires only a digital camera and facial recognition software. Facial recognition software takes measurements of the face like the distance between both eyes, the distance between forehead and cheekbones, etc. Once the data is collected, a sophisticated algorithm converts it into a facial sign that is encrypted. Facial recognition only requires a digital camera and facial recognition software. 

Voice recognition  

Voice recognition technology comes under both types of biometric authentication – behavioral and physiological. Physiologically, the sound emitted by an individual helps in identifying the shape of the vocal tract, such as the nose, mouth, and trachea. When it comes to the biological side, the pronunciation, variations in tone, movement, etc. are taken into account. After combining data from both biometric types, a precise vocal signature is created. In this type, there are chances for inconsistencies due to sickness or many other factors. 

DNA matching  

Human DNA contains information about lineage, race, and health conditions for specific diseases. DNA matching uses the personal physical sample of an individual with his/her consent and is employed as a tool for personal identification to authenticate his/her identity. It is the only existing type of biometrics that is capable of tracing family origin or bloodline. In this method, the personal samples collected include saliva, hair, semen, and blood that contains STRs (Short Tandem Repeat sequences) which confirms the identity of a person after making a comparison between other STRs in the database. 

Behavioral biometrics 

Behavioral biometrics studies and measures behavior patterns or body functions. Here are some of the behavioral biometric forms: 

Gait  

Gait refers to a mix of cyclical and coordinated movements that cause a person to move. Human gait is considered a unique feature to recognize a familiar person. Gate Biometrics maps patterns through computer-based imaging and then converts the recorded data into an arithmetic problem. This type of biometric is the best option for large-scale surveillance as it can easily recognize distant people. 

Lip movement  

Just as a deaf person tracks the movement of the lips to interpret what people are saying, biometric lip movement authentication analyzes and maps the movements of the muscles around the lips precisely, to identify if they are following the desired pattern. Such lip motion sensors often ask users to pronounce passwords and record associated lip movement to facilitate access. 

Signature recognition  

This biometric measures factors such as pressure exerted on the pen, spatial dimensions, and pen stroke in applications (offline and online). The measurements are tracked by a digital tablet and generate a biometric profile automatically using the data, for future authentication. 

Keystroke  

Keystroke Dynamics records the rhythm while entering a password. It measures the time taken to click each key, the delay between keys, and the number of characters typed in minutes. Keystroke patterns improve security and take basic passwords to another level.  

Wrap up 

Every person is unique as well as different. Even if takes identical twins as an example, they also have behavioral patterns and physical characteristics that make them unique. Biometric technology uses these unique characteristics for identity verification and security enhancement. Since biometric access control systems have carriers of information with them, it offers great convenience for users as information will not be lost or forged. Different from the password and username combination, it is very difficult to initiate unauthorized access to documents or data if you are using biometric credentials such as face recognition, or fingerprint scan. This can avoid data breaches to a longer extent and that makes biometric systems very much more reliable than any other security option. These different types of biometrics make it simpler, faster, and more precise to secure identity verification. 

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Disruptive Technology Examples That’ll Shape The Future

Posted on by Mubarak Musthafa

Although the term disruptive technology may represent pictures of high-tech progress in the field of technology, there are many examples of disruptive technology which are used every day by us without even having thought of it. As we know, human progress has evolved over time with the emergence of new disruptive technologies. With endless information at our fingertips and unlimited ways to link and interact instantly, you cannot help but wonder about the future. This article aims to provide a piece of in-depth information about disruptive technologies – defining what the term actually means and examples of disruptive technologies. 

What is disruptive technology? 

The simplest definition of disruptive technology is a technological advancement that drastically changes the way consumers, businesses, or enterprises function on a regular basis. The already existing models are replaced with disruptive technology due to its improved features. However, it should be noted that in certain cases, disruptive technologies do not fully eliminate the technologies they replace. For example, the television has replaced radio as a source of information and entertainment but it did not eliminate the existence of radio. Later, after a point in time, another disruptive technology will replace television as well. Over time, those disruptive inventions that are successful become the norm, and in due course, these disruptive technologies are replaced by new ones. 

Disruptive Technologies – Examples 

There are a lot of examples of disruptive technology. We have already pointed out disruptive technologies like the radio and television. Now we are going to discuss some of the examples of disruptive technology that are shaping the current world.  

Cloud computing  

One of the best examples of disruptive technologies is Cloud which has become very common in our day-to-day lives. Cloud computing businesses not just store and analyze a huge quantity of data, but also support videos, music, and video games streaming to consumers. Moreover, users can access strong computer systems to finish complex tasks through the Internet. Cloud computing has also contributed to many pristine opportunities that provide services that are unique to businesses with the help of cloud delivery models like SaaS, PaaS, and IaaS. Netflix, Google Docs, and Spotify are some of the platforms that take advantage of the cloud. 

Blockchain  

Blockchain technology has received massive attention as it is at the heart of cryptocurrency. Anyhow, blockchain technology has a lot of potential and has started to disrupt the financial services sector. Blockchain uses a decentralized network as a ledger that distributes across numerous devices. Bitcoin, the first application of Blockchain, has developed to disrupt the banking industry, where ledgers are highly consolidated in a particular bank or consortium of banks. The 3 key attributes of blockchain include: 

  • Decentralization 
  • Transparency 
  • Security 

These three features aim to minimize the fees imposed by banks while making secure financial transactions. The main objective was to facilitate speedy transactions free from restrictions and the risks of a single authority. Though blockchain is in its early phase of infancy and needs more control, there is no doubt that this technology will continue to develop, playing an important role in changing obsolete and inefficient business practices while delivering wealth and rights back to the people. 

Internet of Things  

IoT or the Internet of Things refers to everything that is connected to the Internet, which facilitates sending and receiving data (intercommunication) over Wi-Fi, cellular data, or Local Area Network (LAN). IoT will witness new relationships developing between objects and other objects, people and objects, and people and other people – all results in making our smoother, efficient, and effective. While IoT relies largely on the cloud, it is also disruptive in its own way. IoT will change our cities to smart cities, which are more efficient, cheap, and safer to reside in – all with the help of sensors. Further developments may see alarms turning off in the morning, a coffee maker brewing coffee, or our refrigerator ordering food whenever there is a shortage of food items. 

Robotic Process Automation  

Nowadays, all types of organizations use different types of automation to increase efficiency, save time, and reduce process costs. Robotic Process Automation (RPA) services use software bots that collect data on how we communicate with different applications and imitate the same behavior with the least human intervention. While not all business processes are RPA-compliant, high-volume, monotonous tasks that follow a set of consistent rules are most suitable for RPA. With the constant evolution of technology, one thing to be sure is that RPA will play a major role in disrupting many of the processes that are widely used and accepted today. 

Artificial Intelligence  

Artificial Intelligence development has already been a part of our lives for decades. It is currently implemented in areas such as the detection of fraudulent activities, spam filtering in emails, video games, and many more applications. Along with improvising the lives of individuals and increasing the efficacy of business operations, it is growing faster than ever with numerous applications. This AI also needs to gather data from the customer’s search histories, previously purchased products, or conversations to identify their preferences in order to provide customized products and experiences. For example, virtual assistants like Google’s Home and Apple’s Siri perform according to the user’s command. They obtain data according to user preferences and the companies use such data to provide customized services to the user.

Similarly, AI-powered autonomous vehicles are designed to provide certain benefits like minimized road accidents, more efficient fuel consumption, and less traffic congestion. While it is hoped that future applications of AI will shape our lives in an easier and more efficient way, there are some who are wary not only of this technology but also of the number of manual jobs that may get replaced. However, not everyone attributes the growth of this industry to ‘humans vs. AI’, but as a combination of the two to build a better world for us. 

Wrap Up 

We have only experienced a small part of what is yet to come. However, one thing is for sure, these five technologies will definitely play a major role in changing society and the world itself as we already know. 

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Cloud Service Models (SaaS, PaaS, IaaS, IDaaS, NaaS) Explained

Posted on by Mubarak Musthafa

Cloud Computing: An introduction 

Cloud services have recently become one of the leading platforms, mainly because of giant companies such as Google, Microsoft, and Amazon paving the way to advanced technology. Companies hand over their responsibilities to trusted cloud consulting service providers who outsource their storage, supports basic facilities, and ensure security, rather than depending on their own servers.  

Cloud computing involves the distribution of computing resources such as infrastructure, data storage, databases, software, networking, and intelligence that are through the internet. This offers flexible resources, quick innovation, and a financial scale. The service provider manages every technical aspect related to any service.  

Models of Cloud Services 

The cloud servicing model mainly falls into 5 categories – Software as a service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), Identity as a Service (IDaaS), and Network as a Service (NaaS). Let’s talk about each service model in detail. 

Software as a Service (SaaS)

SaaS is identified as a software distribution model or a web platform on the Internet that offers clients access to cloud computing based on a subscription. Thus, came to be known as ‘On-Demand Software’ or ‘Pay-You-Go-App’. The software will be distributed regularly like a service, rather than buying the solution once, like purchasing a product. Through SaaS providers, the customers get licensed services. SaaS is one of the fast-growing services and thus will shift to the role of active cloud service technology mandatory for all organizations and companies. Therefore, it is essential for both the users and buyers to understand the use and compatibility of SaaS. 

Platform as a Service (PaaS)

PaaS is defined as a cloud computing platform built for programmers to create, test, run and control applications. It is mainly designed in a way that makes building websites or mobile applications easier and simpler for developers with no worries about fixing or handling the infrastructure required for development including the servers, data storage, networking, and databases. In PaaS, billing is charged only for platforms used by users for the length of time the services have been used. Unlike desktop solutions, you do not have to pay for any excessive activity. PaaS bears a striking resemblance to SaaS, except that SaaS easily distributes software through the internet, without having to purchase or maintain web development. As opposed to this, PaaS offers a platform for developing software delivered through the web. The point-and-click feature in PaaS enables non-programmers/ non-professionals to create web applications. 

Infrastructure as a service (IaaS)

IaaS, otherwise known as Hardware as a Service (HaaS), is a computing infrastructure controlled through the Internet. One of the key benefits of using IaaS is that it allows consumers to eliminate the expense and complications in buying and controlling the physical servers. IaaS offers many ready-to-use features like the exact environment for development, personal networks, safe storage of data, performance tracking, etc. for the IT infrastructure of businesses. Companies do not have to develop and safeguard their own IT infrastructure, instead, they use third-party servers and cloud backup storage to completely strengthen the development process. Changing the infrastructure of your company to an IaaS solution can contribute to minimizing maintenance of local data centers, reducing costs on hardware, and achieving real-time business insights. IaaS Solutions also provides you with the flexibility to increase or decrease the scope of your IT resources as required. They help you get new applications faster and improve the reliability of your infrastructure. 

Identity as a Service (IDaaS)

IDaaS (Identity as a Service) enables employee or consumer identity details management as a digital entity. This reduces the complexity in remembering and managing different usernames and passwords or deactivating the account and credentials when an employee leaves the organization. The main objective of an identity service is to make sure that the consumers are what they affirm to be and to provide them with the proper access at the exact time to any software application, files, or other resources. If the basic facilities are made on-site for this to happen, then the company needs to find out what to do each time a problem arises. Deploying a centralized cloud-based system developed by identity experts who have dealt with such problems before and resolved them for hundreds of companies is very simple. 

Network as a Service (NaaS)

NaaS (Network as a Service) is a cloud service model that enables clients to directly gain access to the network infrastructure and is based on pay as you use model. It also allows networking services to be taken on lease from a cloud vendor rather than fixing their own network infrastructure by the customer. This service utilizes virtualized network infrastructure and gives safe network services to employees and clients. NaaS service providers always attempt to maintain and handle network resources in a way that reduces the workload of clients/employees. NaaS facilitate consumers to run their own networks with no maintenance on their own networking infrastructure. Similar to cloud services, NaaS providers operate networking activities with software and internet connection, thus enabling companies to establish their own networks with no hardware.  

Wrap Up 

Cloud computing has emerged as one of the rapidly growing marketing trends for the past five years, and it is certain that this trend will become relevant in the decade to come. As leading cloud computing companies such as Amazon, Microsoft, and Google invest in technology, it is clear that the cloud will be more innovative with each new release. In fact, cloud computing is there in the field of technology for a long time. It comes with a set of criteria that provide a specific set of web services to customers.  

The very first thing you need to do is to find the best cloud service providers with certified security systems, diverse functionalities, and a low-cost pricing model. Once you identify the right model for your company and business, you forward with it.  

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Cloud Computing in Banking

Posted on by Mubarak Musthafa

Since the beginning of cloud computing, there have been a number of organizations that are gradually migrating from the banking world to it. The amount of data generated and used in the economic sector is increasing at a rapid pace. Cloud computing in the banking sector offers compatibility, safe storage capacity, 24 × 7 operating hours, etc. The inception of cloud-based software has provided many advantages to organizations from different sectors. Of all the major sectors, the banking industry has taken most of the benefits due to cloud technology.  

Let us know more about the influence of cloud computing in the banking sector.  

Benefits of cloud computing 

We have mentioned that cloud computing offers a lot of benefits to the banking sector. Here are some of them: 

Improved data security  

The economic impact of a breach in data security is indisputably one of the most critical consequences which may lead to the loss of millions of dollars. Even during this increasing amount of data breaches, most financial institutions still depend on on-site systems. The advent of cloud computing has brought a better option to prevent data fraud by providing an up-to-date customer-oriented platform with outstanding security features to protect banking data. 

Better cost optimization  

One of the major challenges faced by many organizations while using a legacy on-premise system is difficulty in adapting to routine organizational changes. With cloud computing, financial institutions will be able to meet their ever-changing needs: convert a huge leading capital expenditure into small and current operating costs, avoid cost on infrastructure, server space, and resources for IT staffs. Moreover, this distinctive nature of cloud computing enables banking institutions to pick the services they need on a pay-as-you-use model. 

Increased efficiency

The cloud infrastructure in banking enables financial firms to accelerate operations with better efficiency. The buyers and sellers can be connected on a common platform in order to simplify the payment processes. It helps to speed up transactions and track data easily. 

Business continuity

Cloud computing offers financial institutions including banks and other firms with high data protection, error tolerance, and revival of organizations from disaster. It offers redundancy at a higher level and backup at a relatively low cost compared to traditional handling solutions.  

Business agility and focus  

As cloud infrastructure offers flexibility, the development cycle of new products in financial institutions will take only a short time period. It gives faster and more efficient assistance to banking customers who are in need of the bank’s service. There is no need for investment in infrastructure as the cloud is available on a pay-as-you-go basis, which saves time for initial setup as well. Cloud computing does not support capital investment while proceeding with a new product.  

Green IT  

Organizations can shift their services to a virtual environment by using cloud computing. This not only minimizes the consumption of a large amount of energy but also reduces the carbon footprint obtained while building a physical infrastructure, which leads to more efficient use of computing power and low idle time. 

Cloud computing – Challenges  

Though cloud computing offers these many benefits in the banking sector, why are most financial institutions still doubtful of employing it? It is because of the challenges they may face while implementing it in their organizations. Here are some of them: 

Data security and privacy  

Security remains a primary concern in any organization and it has utmost importance when it comes to financial institutions. Bank data contains strategic information or sensitive data and protecting it from security breaches is paramount for the banking sector. Every technology has security issues and as a technology cloud computing is no different from that. In the cloud, security breaches can be prevented to a larger extent but cannot be avoided completely. 

Regulatory and compliance  

A bank must adhere to strict standards which cannot be compromised under any circumstances. Most of the banking regulators may need the financial data of a customer living in the same nation. For some compliance regulations, valuable data on common servers or databases may not be combined with other data. Therefore, banks need to have an exact idea about the given environment where their information lies. 

No complete control  

Financial organizations have major concerns while controlling crucial business applications and a large amount of customer data. Transferring it to a third-party cloud service provider may compromise the flexibility and agility it offers. Therefore, not having control over large amounts of data discourages companies from transferring to the cloud. 

Factors to be considered while implementing cloud in banking 

Define ROI 

Banks should be careful not to invest heavily in cloud computing until clear benefits are available. The first step is getting a detailed explanation from cloud service providers about the costs and repercussions of transferring current banking applications and infrastructure to the cloud. Bank has the full rights to validate the Return on Investment (ROI) it may get from implementing cloud technology. 

Choose expertise service providers  

Banks must use a roadmap to handle cloud service delivery programs. Financial organizations can look for service providers who have proven experience in pilot projects and have expertise in cloud computing ventures. Banks can begin with small and simple applications like CRM (Customer Relationship Management) and can jump on to major business applications later. 

Understand data confidentiality and regulatory requirements  

To make the best use of cloud computing and to make informed decisions, banks need to have a better understanding of privacy and regulatory issues. In order to meet local regulations and client privacy requirements, banks are required to store sensitive data within firewalls. For this reason, private cloud service models are a better option when compared to the public or a hybrid cloud operating model. Due to the increasing trust of the consumers in the public cloud model, there are chances for banks to shift to the public model in the near future. 

Wrap Up 

Banks were slow to adopt cloud computing due to many challenges which may result in reliability issues and security risks. At the same time, banks also wanted to secure their financial information through restricted access. This can be done by cloud computing only. Nowadays banks offer a number of services, as they have different needs based on the migration of applications to the cloud. Cloud computing enables banks to generate more dynamic and flexible business promises for emerging and competitive markets and supports them to change their business processes.  

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