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Top 4 Applications of Cognitive Robotic Process Automation

Top 4 Applications of Cognitive Robotic Process Automation

With the dawn of automation, industries all over the world are depending on robots to carry out tasks, such as product designing and manufacturing. It optimizes repetitive processes and improves cost efficiency. Incorporation of cognitive capabilities, like natural language processing and speech recognition into robotic process automation has resulted in the birth of Cognitive Robotic Process Automation (CRPA). Let’s delve into the current applications of this revolutionary technology.

Finance and banking sector:

Customers are demanding expedient methods to transfer money and make investments.  Also, the volumes of customer data are increasing rapidly. Hence, banks need to improve the speed of information processing. To achieve this, they have turned to process automation. Many banks are adopting AI-powered technology to automate regular processes.

According to a survey conducted by BIS Research, Banking and Finance sector is likely to become the largest revenue generator in the world for CRPA industry. For example, Bank SEB in Sweden bought cognitive robotic process automation software from IPsoft, a foremost company of CRPA industry. This technology is actually a software robot named Amelia that has knowledge of 20 different languages and is aware of semantics, including English and Swedish. In case Amelia fails to solve the problem at hand, it transfers the same to a human operator, and studies the interaction to hone its skills and apply it to similar cases in future.

U.K.’s KPMG has collaborated with Automation Anywhere to provide digital staff for clients.


Task like manual inputs, data gathering and retrieval, legacy applications and system updating is very time consuming. Hence, the insurance industry is welcoming automation in its processes. This help with the following tasks:

  • Automates fraud detection, policy renewal and premium calculation
  • Improves customer service
  • Enhances employee engagement
  • Upgrades business productivity as software robots can work for hours at a stretch
  • Frees employees for important tasks that need manual handling

Developed economies, including U.S. and the European nations are extensively employing RPA/CRPA bots. AXA Group, one of the chief French insurance companies using smart automation services to improve its bankroll, reported that France has the fifth highest insurance premiums in the world.

Leading IT service provider of Australia, DXC Technology, has partnered with Blue Prism, one of the best companies providing RPA solutions, to improve the RPA capabilities for key insurance clients, like Australia and New Zealand Banking Group (ANZ). Fukoku Mutual Life Insurance, top insurance firm of Japan, has replaced 30 human workers with IBM’s latest AI tech, Watson Explorer. The tech’s deployment has boosted company savings and enhanced productivity by 30%.


Telecom and IT Industry:

Business process outsourcing (BPO) services are facing problems like increased operational costs and low profit margins. RPA/CRPA software bots can be one of the ways to tackle this problem. Hexaware Technologies, a topnotch company in this field, has partnered with Workfusion to evolve IT infrastructure, combat the aforementioned problems and boost overall productivity.


Some of the challenges of the healthcare industry are:

  • Maintaining paper records of patients’ medical documents.
  • Transferring these records to digital databases
  • Manually updating databases
  • Maintain an inventory database for medicinal supplies
  • Systematic management of unstructured data
  • Innovation in healthcare encounter regulatory and reporting challenges when launching new drugs.

These tasks are repetitive and increase chances of errors when done manually. Automation helps tackle these problems and also provide safe and good quality drugs to the market. Blue Prism is one of the principal providers of RPA for healthcare.

Future Scope:

Competition in the global capital markets is increasing. New contestants are bringing in ‘’disruptive technologies’’ that are pressurizing existing institutes to increase their efficiency and cut down costs. Hence, the need to embrace cognitive automated technology.

Australia and Japan are among the top countries adopting process automation. Leading countries embracing RPA for financial services include India, China and Singapore. It is expected that Fintechs will mainly disrupt three areas of financial sector-consumer banking, investment handling, fund and payment transfer.

It is about time that all businesses and organizations integrate machine learning and artificial intelligence in their processes for competitive advantage.

How can you take advantage of this tech-driven era? Enroll for machine learning training in Delhi at DexLab Analytics. Many top companies look for expertise in this budding technology while recruiting employees. DexLab’s Machine learning course in Delhi offers superior guidance that will help you develop crucial knowledge needed to stay ahead of competition.


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How Some Astonishing Techs of 2018 are Influencing Development in Africa

How Some Astonishing Techs of 2018 are Influencing Development in Africa

The technological evolution is conquering things which we previously considered to be strictly human. Addressing the scope of current tech space, we can say that the possibilities are endless, quite literally. AI is accomplishing tasks we never thought were feasible, such as composing music and creating videos. With the help of analytics, doctors can predict the effectiveness of cancer treatments in days; whereas earlier precious months were wasted to determine the same. In Africa, AI-powered technology will soon be employed to tackle burning social problems.

So, let’s take a look at some current tech trends that are inspiring development in Africa.

New data sources:

The availability of new sources of data plays a pivotal role in current tech advancements. For example, consider the financial inclusion of a nation’s population. Traditionally, financial institutes analyze financial history of an individual to determine if the person is eligible for a loan or not; which includes credit scores, IDs and other relevant documents. However, this excluded a large portion of the population who never had the chance to build a credit history due to the absence of any form of documentation. FinTech companies in East Africa have adopted a different approach-they are analyzing available mobile data about a person, like the frequency of getting a recharge, and making lending decisions on the base of this data. Thus, more people are now being able to access financial services and accomplish their goals, like educating children or starting businesses.

New features in SAS platform have the ability to analyze images. This is benefiting rangers working for wildlife conservation as previously they would have to manually sort the pictures of animals into species and sexes. Now, SAS’s new AI-driven technology can do the classification and rangers can focus on more important tasks.

Improved predictions:

Africa is seeing the emergence of new machine learning algorithms, like the extreme gradient boosting model, which are allowing data scientists to make more precise predictions. In Nigeria, this is boosting the development of models that prevent customer churn in the telecom industry. These models assess customer information, like billing data, purchase history, demographics and service usage, and create loyalty profiles that enable better marketing campaigns.

Bring into play the unstructured data pool:

Generally, companies crunch data from structured data sources, like transactional data. However, tapping into unstructured data sources, like customer complaints, reviews and text information, can be highly advantageous for businesses. These data sources help predicting customer churn more accurately.

Plunging into Deep Learning:

Deep learning falls under the category of machine learning, which is creating waves of excitement all over the world. It has the ability to model complex concepts in data through the use of high-level structures, algorithms and multiple processing steps. Deep learning teaches computers to recognize patterns through the numerous processing steps and perform tasks that are conventionally carried out by humans, such as image identification and speech recognition.

These models are improving traditional techniques used in credit risk modeling and fraud detection. SAS has collaborated with Equifax to implement deep learning models for improved risk management.

Nigeria has turned its focus on upskilling its people in data science, so that they can take advantage of this AI-era and become an outsourcing hub for deep learning projects.

Emotionally intelligent bots:

An exciting application of AI and language processing is chatbots. They are programmed to enable conversations between machines and humans. This helps save a lot of time and money that was previously wasted on performing repetitive and mundane tasks, such as responding to customer queries related to their bank accounts.

Recently, United Bank of Africa launched its chatbot, named Leo, which is in fact a Facebook bot that allows bankers to carry out real-time transactions and other banking activities, like opening accounts and paying bills.

Thus, we are entering an era where machines can think and learn utilizing the power of AI. AlphaGo, a programme created by Google in 2016, has been able to defeat the best human players of this ancient Chinese game. And AlphaGo Zero, the next version, learned by playing against itself and after a period of time defeated AlphaGO.

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Transforming Society with Blockchain and Its Potential Applications Worldwide

Transforming Society with Blockchain and Its Potential Applications Worldwide

According to Google Search, ‘blockchain’ is defined as “a digital ledger in which transactions made in bitcoin or in other cryptocurrency is recorded chronologically and publicly.”

Speaking in a way of cryptocurrency, a block is a record of new transactions that could mean the actual location of cryptocurrency. Once each block has completed its transaction, it’s added to the chain, creating a chain of blocks known as blockchain.

Suppose a Google spreadsheet is shared by each and every computer which is connected to the internet in this world. When a transaction happens, it will be recorded in a row of this spreadsheet. Just like a spreadsheet has rows, Blockchain consists of Blocks for each transaction.

Whoever has access to a computer or mobile can connect to the internet and can have access to the spreadsheet and add a transaction, but the spreadsheet doesn’t permit anyone to edit the information which is already available. No third party can interfere into its transactions, therefore saves time and conflict.


Types of Blockchains:

  • Open and permission-less: Public and permissionless blockchains look like bitcoin, the first blockchain. All exchanges in these blockchains are open and no authorizations are required to join these circulated elements.
  • Private and permission: These blockchains are constrained to assigned individuals, exchanges are private, and authorization from a proprietor or supervisor substance is required to join this system. These are frequently utilized by private consortia to oversee industry esteem chain openings.
  • Hybrid blockchains: An extra region is a developing idea of sidechain, which takes into consideration distinctive blockchains (open or private) to speak with each other, empowering exchanges between members crosswise over blockchain systems.

Various Applications Of Blockchain Are As Follows:

a) Smart Contracts:

Smart Contracts eases the way we exchange money, property, shares and avoids third person/party conflicts. Smart keys access can only be permitted to the authorized party. Basically, computers are given the command to control the contracts and to release or hold the funds by giving the keys to the permitted persons.

For example, if I want to rent an office space from you, we can do this in blockchain using cryptocurrency.  You will get a receipt which is saved in the virtual contract and I will get the digital entry key which will reach me by a specified date. If you send the key before the specified date, the function holds it and releases both receipt and the key when the date arrives.

If I receive the key I surely should pay you. And this contract will be canceled when the time gets complete, and it cannot interfere as all the participants will be alerted. The Smart contracts can be used for insurance premiums, financial derivatives, financial services, legal processes etc.

b) Digital Identity:

The future of blockchain will be blooming in the coming years. Blockchain technologies make both managing and tracking digital identities reliable and systematic, resulting in easy registering and minimizing fraud.

Be it national security, citizenship documentation, banking, online retailing or healthcare, identity authentication and authorization is a process entangled in between commerce and culture, worldwide.  Introducing blockchain into identity-based mechanisms can really bring captivating solutions to the security problems we have online.

Blockchain technology is known to offer a solution to many digital identity issues, where identity can be uniquely validated in an undeniable, unchangeable, and secured manner.

Present-day methods involve problematic password-based systems of known secrets which are exchanged and stored on insecure computer systems. Blockchain-based certified systems are actually built on undeniable identity verification for using digital signatures based on the public key related cryptography.

In blockchain identity confirmation, the only check that is performed is to know if the transaction was signed by the authorized private key. It is implied to whoever has access to the private key is the owner and the exact identity of the owner is deemed unrelated.

c) Insurance:

Claims dealing can be disappointing and unrewarding. Insurance agents need to go through deceitful cases and deserted approaches, or divided information sources for clients to express a few – and process these documents manually. Space for mistake is enormous. The blockchain gives an ultimate framework for hazard-free administration and clarity. Its encryption properties enable insurers to represent the ownership to be protected.

“This will be the toughest on the portions of the industry that are least differentiated, where consumers often decide based on price: auto, life, and homeowner’s insurance.” — Harvard Business Review

d) Supply-Chain Communications and Proof-of-Provenance:

The majority of the things we purchase aren’t made by a single organization, yet by a chain of providers who offer their ingredients (e.g., graphite for pencils) to an organization that gathers and markets the final commodity. On the off chance that any of those commodities flops, in any case, the brand takes the brunt of the backfire — it holds most of the duty regarding its supply chain network.

However, consider the possibility that an organization could proactively give carefully perpetual, auditable records that show stakeholders the condition of the item at each esteem included process.

This is not a little task: The worldwide supply chain network is evaluated to be worth $40 trillion; and from a business-process point of view, it’s a fabulously incapable chaos. As a related issue, blockchain can be utilized to track diamonds, creative skill, real estate, and practically any other resources.

e) Music Industry:

While music lovers have hailed digitization as the popular government of the music business, 15.7 billion dollar music industry is confusingly continuing as before. Music piracy through unlawfully downloaded, duplicated and shared content eats into the artist’s sovereignties and music labels’ income. Added to this, is the absence of a vigorous rights administration framework, which prompts loss of income to the artist.

Also, the income, when it really achieves the artist, can take up to two years! Another region of concern is unpaid sovereignties, which are frequently suspended in different stages because of missing data or rights possession. There is additionally an absence of access to continuous advanced sales information, which if accessible can be utilized to strategize advertising efforts more successfully.

These very zones are the place Blockchain can have stunning effects. As a publically accessible and decentralized database that is distributed over the web, Blockchain keeps up lasting and undeletable records in cryptographic format. Exchanges happen over a peer to peer system and are figured, confirmed and recorded utilizing a computerized agreement strategy, disposing of the requirement for an intermediator or outsider to oversee or control data.

The very engineering of Blockchain being unchanging, dispersed and distributed conveys enormous potential to manage the present troubles influencing the music business.

An essential region in which Blockchain can bring out positive change is in the formation of a digital rights database. Digital rights articulation is one of the basic issues distressing the present music industry. Recognizing copyright of a melody and characterizing how sovereignties ought to be part of musicians, entertainers, distributors, and makers are troublesome in digital space. Regularly artists miss out on sovereignties because of complicated copyright condition.

Blockchain’s changeless distributed ledger framework, which guarantees that no single organization can assert proprietorship, ensures an ideal arrangement. Secure documents with all applicable data, for example, structure, versus, straight notes, cover craftsmanship, permitting, and so on, can be encoded onto the Blockchain making a changeless and inerasable record.

f) Government and Public records:

The administration of public services is yet another region, where blockchain can help diminish paper-based procedures, limit fraud, and increment responsibility amongst specialists and those they serve.

Some US states are volunteering to understand the advantages of blockchain: the Delaware Blockchain Initiative propelled in 2016, expects to make a proper legitimate foundation for distributed ledger shares to increase productivity and speed of consolidation administrations.

Illinois, Vermont, and different states have since reported comparative activities. Startup companies are sponsoring in the effort also: in Eastern Europe, the BitFury Group is presently working with the Georgian government to secure and track government records.


This article focused on the blockchain and its applications in various industries explains challenges and potentials and how people can secure their information digitally without any issues and increasing their ability. As these applications are still under development and yet to be untangled in the future, blockchain could become a powerful tool conducting fair trade, improving business and supporting the society.

To never miss a beat of technology related news and feeds – follow DexLab Analytics. We are a team of experts offering state of the art business analyst training courses in Gurgaon. Not only that, we provide a plethora of machine learning and Hadoop courses too for all the data-hungry candidates. So, drop by and quench your thirst for data from us!

About the Author:

K.Maneesha is an SEO Developer At She holds a masters degree in Marketing from Alliance University, Bangalore. Maneesha is a dog-lover and enjoys traveling with friends on trips. You can reach her at Her LinkedIn profile Maneesha Kakulapati.


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Python Machine Learning is the Ideal Way to Build a Recommendation System: Know Why

Python Machine Learning is the Ideal Way to Build a Recommendation System: Know Why

In recent years, recommendation systems have become very popular. Internet giants, like Google, Facebook and Amazon, use algorithms to tailor search results to customer preferences. Any system that has a search bar collects data on a customer’s past behavior and likings, which enable these platforms to provide relevant search results.

All businesses need to analyze data to give personalized recommendations. Hence, developers and data scientists are investing all their energies and mental faculties to come up with perfect recommendation systems. Many of them are of the opinion that Python Machine Learning is the best way to achieve this. Often, building a good recommendation system is considered as a ‘rite of passage’ for becoming a good data scientist!

Delving into recommendation systems:

The first step in the process of building a recommendation system is choosing its type. They are classified into the following types:

  • Recommendation based on popularity:

This is a simplistic approach, which involves recommending items that are liked by the maximum number of users. The drawback of this approach is its complete exclusion of any personalization techniques. This approach is extensively used in online news portals. But in general, it isn’t a popular choice for websites because it bases popularity on entire user pool, and this popular item is shown to everyone, irrespective of personal choice and interest.

  • Recommendation based on algorithms:

This process uses special algorithms that are tailor-made to suit every customer. They are of two types:

  • Content based algorithms:

These algorithms are based on the idea that if a person likes a product then he/she will also like a similar product.  It works efficiently when it is possible to determine the properties of each product. It is used in movie and music recommendations.

  • Collaborative filtering algorithms:

These algorithms are dependent on past behavior and not on properties of an item. For example, if a person X likes items a, b, c and another person Y likes items b, c, d, then it is concluded that they have similar interests and X should like item d and Y should like item a. Because they are not dependent on additional information, collaborative filtering algorithms are very popular. E-commerce giants, like Amazon and Flipkart, recommend products based on these algorithms.

After choosing the type of recommendation system to build, developers need to locate relevant datasets to apply to it. The next step is determining the platform where you’ll build your recommendation system. Python machine learning is the preferred platform.

Let’s Take Your Data Dreams to the Next Level

Advantages of using Python Machine Learning:

  • Code: Python makes the process of writing code extremely easy and working with algorithms becomes quite convenient. The flexible nature of this language and its efficiency in merging different types of data sets make it a popular choice for application in new operating systems.
  • Libraries: Python encompasses a wide range of libraries in multiple subjects, such as machine learning and scientific computing. The availability of a large number of functions and methods enables users to carry out several actions without having to write their own codes.
  • Community: Python includes a large community of young, bright, ambitious and helpful programmers. They are more than willing to provide their valuable inputs on different projects.
  • Open source: The best part about Python is that it is completely open source and has sufficient material available online that will help a person develop skills and learn essential tips and tricks.

Proficiency in Python is highly advantageous for anyone who wants to build a career in the field of data science. Not only does it come handy in building complicated recommendation systems, it can also be applied to many other projects. Owing to its simplicity, Python Machine Learning is a good first step for anyone who is interested in gaining knowledge of AI.

In the current data-driven world, knowing Python is a very valuable skill. If one’s aim is to collect and manipulate data in a simple and efficient manner, without having to deal with complicated codes, then Python is the standard.

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How is AI Shaping the Indian Job Market?

How is AI Shaping the Indian Job Market?

Currently, startups focusing on Artificial Intelligence, Machine Learning and Deep Learning are on the rise in India. According to a recent report by AI Task Force, there are 750 startups in India that are actively working to build a robust AI ecosystem in India. Initiatives to promote AI by Indian government include establishment of NITI Aayog, the policy think tank of India, and Digital India, which is a campaign to improve technological infrastructure of the country.

65% of participants of a PwC survey believed that AI will have a grave impact on the employment scenario of India. Interestingly, the majority of participants of this survey were of the opinion that AI will allow employees to do more value-added tasks as it will take up all the daily mundane tasks.

Deep Learning and AI using Python

Job market outlook:

‘’We expect a 60 per cent increase in demand for AI and machine learning specialists in 2018’’, said BN Thammaiah, Managing Director, Kelly Services India. Belong, a Bengaluru-based outbound hiring firm startup, shares the same view, stating that the demand for AI professionals has risen by leaps and bounds due to the widespread adoption of AI and automation technologies across companies. Consulting industry leader, Accenture, expects AI to add $957 to India’s GDP by 2035.

Jump in demand:

Only 4 percent of AI professionals have work experience in core domains, like deep learning and neural networks.

For every 1000 jobs in the field of Deep learning, there are approximately 530 professionals available. Similarly, for every 1000 jobs in the field of Neuro-linguistic Programming (NLP), there are only 710 professionals available.

The lack of core data science disciplines in engineering institutes across the country is responsible for the disparity between demand and supply of AI professionals. Only a few selected institutes, like IITs and IISc, have ML programs in their curriculum. The active AI researchers in India are a meager 386 in number.

AI hotspots in India:

AI-work hubs in India are Bengaluru, New Delhi and Mumbai. IBM, Microsoft, Flipkart and Amazon are carrying out good research work in AI. Companies like Adobe, Accenture, Amazon, JP Morgan, SAP, L&T Infotech, Nvidia, Intel and Wipro are actively hiring AI professionals. The main sectors fostering AI employment are e-commerce, banking and finance. Kamal Karanath, Co-founder of Xpheno, a recruitment company, said that there would be a huge demand for AI engineers in these sectors in the next 5 years. AI-powered technology boosts efficiency and security of Indian banking and financial sector.

India Inc is endeavoring to upskill workers in subjects like machine learning, cloud computing and big data. In efforts to nurture talent and obtain solutions from vertical focused AI startups, which are developing innovative technologies, enterprises have set up many accelerator programs. Flipkart is developing AI products that will boost their business growth.


A peek into the future of AI:

The Indian government intends to establish research institutes and Centres of Excellence that foster training and skilling in fields like AI, robotics, big data analysis and internet of things. Top engineering schools, like IITs, IIITs and IISc are collaborating with industries to bridge the gap in AI talent, provide targeted solutions and steer growth of the AI industry. Government of India is framing numerous policies to promote industry-academic partnerships.

Get an edge in this AI-era by enrolling yourself for the Machine Learning training course at DexLab Analytics– a leading data analyst training institute in Delhi.


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Evolving Logistics Scenario: The Tech-driven Future of Logistics Industry

Customer expectations are growing by the day; they are demanding faster and more flexible deliveries at minimum delivery costs. Businesses are being pressurized to customize their manufacturing processes as per customer demands. This is a hard slog for the logistics industry, which has to keep delivering better services but for lower prices.

The logistics industry can only achieve this through ‘digital fitness’. It has to make intelligent use of the global wave of digitization, including data analytics, automation and ‘Physical Internet’. The Physical Internet is an open global logistics system that is transforming the way physical objects are handled, moved, stored and supplied. It aims towards the replacement of current logistical models and making global logistics more efficient and sustainable. The Physical Internet promises better standardization in logistics operations, including shipment sizes, labeling and systems.

The central theme in logistics sector is collaborative working, which enables market leaders to retain dominance.

Now, let us take a look at a few tech-driven domains that will shape the future of logistics.

The future of Logistics Lies in IoT

Internet of Things has been the most innovative technology of the present era. It has the potential to revolutionize the logistics sector. The key benefits of IoT with regard to logistics are:

  • Real-time alerts and notifications
  • Automate processes that gather data from various machines
  • Automate vital operations like inventory management and asset tracking: With the help of IoT, companies can improve tasks like tracking orders, determining what items need to be stocked up and how certain products are performing.
  • Able to function without any human interventions.
  • Logistic companies can provide safer deliveries
  • Enable the regulation of temperature and other environmental factors.

IoT will be advantageous for the entire logistics sector, including fleet and warehouse management, and shipment and delivery of products. IoT can help companies dealing with cargo shipments by improving visibility in the delivery and tracking of cargo.

Warehouse Automation

Warehouse automation is set for a major overhaul. Online shopping is thriving and logistics, especially warehouse operations, need to be more refined and speedy. Warehouse operations of many e-commerce giants are undergoing a robotics makeover. According to reports, the market for logistics robotics, which had generated revenues worth 1.9 billion USD in 2016, is likely to generate sky-high revenues worth 22.4 billion USD this year.

The advancements in robotics include programming robots to pick and pack goods, load and unload cargo and at times deliver goods too. Employing robots speed up the processes of data collection, maintaining records and managing inventories.  Most importantly, robots leave no room for human errors in the processes.

Blockchain Technology in Logistics

The growth of crypto-currencies like Bitcoin has popularized blockchain technology. Blockchain being a type of distributed ledger technology provides secure, traceable and transparent transactions. Blockchain technology employed by logistics firms will improve customer visibility into shipments and help prevent data breaches.

In the present times, logistics is considered the backbone of a stable economy. Thus, for India to emerge as a superpower, the logistics market needs to be developed and integrated with state-of-the-art technologies. Conducive policies and a healthy partnership between private and public sector is crucial to steer India into an era of competent and cost-effective business operations.

In times to come, automation will transform every industry. Don’t be left behind. Get an edge by enrolling for the data science and machine learning certification course at the premier data analyst training institute in DelhiDexlab Analytics.


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How Conversational AI and Chatbots are Revolutionizing Indian banking Industry

Thanks to the advancements in AI and ML, bank work can now be done with the click of a phone button! Innovations in the field of customer services form an important part of the technology overhaul. The banking sector is making hefty investments on AI technology to simplify user experience and enhance overall performance of financial institutes.

Let’s take a look at how conversational AI and chatbots are revolutionizing the Indian banking industry.

  • Keya by Kotak Mahindra Bank

Keya is the first AI-powered chatbot in Indian banking sector. It is incorporated in Kotak’s phone-banking helpline to improve its long-established interactive voice response (IVR) system.

‘’Voice commands form a significant share of search online. In addition, the nature of the call is changing with customers using voice as an escalation channel. Keya is an intelligent voicebot developed keeping in mind the customers’ changing preference for voice over text. It is built on a technology that understands a customer’s query and steers the conversation to provide a quick and relevant response”, says Puneet Kapoor, Senior Executive Vice President, Kotak Mahindra Bank.


  • Bank of Baroda chatbot

Akhil Handa, Head of Fintech Initiatives, Bank of Baroda said that their chatbot will manage product-related queries. He believes that the services of the chatbot will result in better customer satisfaction, speedy responses and cost minimization.

  • Citi Union Bank’s Lakshmi Bot

Lakshmi, India’s first humanoid banker is a responsive robot powered by AI. It can converse with customers on more than 125 topics, including balance, interest rates and transactional history.

  • IBM Watson by SBI

Digital platforms of SBI, like SBI inTouch, are utilizing AI-powered bots, such as IBM Watson, to enhance customer experience. SBI stated that modern times will witness the coexistence of men and machines in banks.

  • AI-driven digital initiatives by YES Bank in partnership with Payjo

Payjo is a top AI Banking platform based out of Silicon Valley in California. YES Bank has partnered with Payjo to launch YES Pay Bot, its first Bot using AI, which improves already popular wallet services. The YES Pay wallet service is trusted by more than half-a-million customers.

  • YES TAG chatbot

YES TAG chatbot has been launched by YES Bank and enables transactions through 5 messaging apps. Customers can carry out a wide range of activities, such as check balance, FD details, status of cheque, transfer money, etc. It is currently used in Android and will soon be available on Apple App Store.

  • Digibank

Asia’s largest bank, DBS Bank, has developed Digibank, which is India’s first mobile bank that is ‘chatbot staffed’. It provides real-time solution to banking related issues. This chatbot employs a trained AI platform, called KAI, which is a product of New York startup- Kasisto.

  • Axis Bank launches intelligent chatbot in association with

Axis Bank facilitates smart banking with the launch of a chatbot that employs conversational interface to offer interactive mobile banking solutions. This intelligent chatbot was developed in association with Singapore based AI company- Active AI.

  • HDFC Bank launches OnChat in partnership with

To enable smooth ecommerce and banking transactions, HDFC in partnership with has launched a conversational chatbot, called OnChat. It is available on Facebook messenger even to people who aren’t HDFC customers. Users can recharge phone, book cabs and pay utility bills through this chatbot.

  • EVA by HDFC Bank

EVA is exclusively for the customers of HDFC Bank. It is an electronic virtual assistant developed in partnership with Senseforth, an AI startup based in Bengaluru.

  • mPower by YES Bank

mPower is a chatbot for loan products that has been developed by YES Bank in association with Gupshup-a leading bot company. It assists customers on a variety of loan related topics like personal loans, car loans and loan against securities.

In the future, there will be three kinds of bots- speech-based bot, textbots and video chatbots. Conversational bots work in harmony with human employees to enrich customer experience.

Thus, AI-powered technology is the way forward. To be industry-ready in this AI-era, enroll for the Machine Learning course in Gurgaon at Dexlab Analytics. It is a premier Analytics training institute in Delhi.


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How Artificial Intelligence is Boosting the Design of Smarter Bionics

How Artificial Intelligence is Boosting the Design of Smarter Bionics

Artificial Intelligence and Machine Learning are a set of technologies that empower machines and computers to learn and evolve upon their own learning through constant reiteration and consistent data bank upgrades. The entire chain of mechanism stands on recursive experiments and human intervention.


Advances in technology have greatly benefited the field of prosthetics in the last few years. Today’s prosthetic limbs are made using space-age materials that provide increased durability and function. In addition, many prosthetics make use of bionic technology. These types of prosthetics are called myoelectric prosthetics.

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Myoelectric prosthetics picks up the electrical action potential from the residual muscles in the amputated limb. Upon receiving the action potentials, the prosthetic amplifies the signal using a rechargeable battery. Detected signal can be used further into usable information to drive a control system. Artificial Intelligence helps to identify motion commands for the control of a prosthetic arm by evidence accumulation. It is able to accommodate inter-individual differences and requires little computing time in the pattern recognition. This allows more freedom and doesn’t require the person to perform frequent, strenuous muscle contractions. The inclusion of AI technology in prosthetics has helped thousands of amputees return to daily activities. While technologies that make bionic implants possible are still in infancy stage, many bionic items already exist.

The Bionic Woman

 Angel Giuffria is an amputee actress and Ottobock Healthcare’s Brand Champion who has been wearing electromechanical devices since she was four months old. Following are excerpts of an interview with her.

“I wear currently the bebionic 3 small-size hand which sounds like a car. But at this point, that’s where we’re getting with technology. It’s a multi-articulating device. That small-size hand is really amazing… this technology wasn’t available to them previously”

She further added, “..The new designs that look more tech are able to showcase the technology. I’ve really become attached to and I think a lot of other people have really clung onto as well because it just gives off the impression of showing people how capable we are in society now.”

 She also spoke about prosthetics like the Michelangelo Hand which is stronger, faster and has multiple hook options. Modern additions to prosthetics such as lights and cameras are added advantages. She describes her hand to be able to do multiple functions like change grip patterns and control wrist movements which enable her to hold small items like keys and credit cards.



Bertolt Meyer’s amazing bionic hand controlled via an iPhone app is another glimpse at the advances being made in prosthetics.

In 2009, Meyer, a social psychologist at the University of Zurich was fitted with an i-limb, a state-of-the-art bionic prosthesis developed by a Scottish company, Touch Bionics, which comes with an aluminum chassis and 24 different grip patterns. To select a new suite of gestures, Meyer simply taps an app on his iPhone. He describes his i-limb to be the first, where aesthetics match engineering.


In the world of prosthetics, function is the key. Most amputees are constantly searching for the same level of functionality that they enjoyed before they lost their limb. With the introduction of artificial intelligence in prosthetic limbs, amputees are closer to their goals than ever before. Bionics having access to the relevant databases are capable of learning new things in a programmed manner which improves their performance.

For more such interesting blogs follow Dexlab Analytics. Also take a look at the Machine Learning courses being offered by Dexlab Analytics– a premier analytics training institute in Gurgaon.


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R Programming, Python or Scala: Which is the Best Big Data Programming Language?

R Programming, Python or Scala: Which is the Best Big Data Programming Language?

For data science and big data, R, Python and Scala are the 3 most important languages to master. It’s a widely-known notion, organizations of varying sizes relies on massive structured and unstructured data to predict trends, patterns and correlations. They are of expectation that such a robust analysis will lead to better business decisions and individual behavior predictions.

In 2017, the adoption of Big Data analytics has spiked up to 53% in companies – says Forbes.

The story of evolution

To start with, big data is just data, after all. The entire game-play depends on its analysis – how well the data is analyzed so as to churn out valuable business intelligence. With years, data burgeoned, and it’s still expanding. The evolution of big data mostly happened because traditional database structures couldn’t cope with such multiplying data – scaling data became an important issue.

For that, here we have some popular big data programming languages. Dive down:

R Programming

R Programming is mainly used for statistical analysis. A set of packages are available for R named Programming with Big Data in R (pbdR), which encourages big data analysis, across multiple systems via R code.

R is robust and flexible; it can be run on almost every OS. To top that, it boasts of excellent graphical capabilities, which comes handy when trying to visualize models, patterns and associations within big data structures.

According to industry standards, the average pay of R Programmers is $115,531 per year.

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Compared to R, Python is more of a general-purpose programming language. Developers adore it, because it’s easy to learn, a huge number of tutorials are available online and is perfect for data analysis, which requires integration with web applications.

Python gives excellent performance and high scalability for a series of complicated data science tasks. It is used with high-in-function big data engines, like Apache Spark through available Python APIs.

Their Machine Learning Using Python courses are of highest quality and extremely student-friendly.

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Last but not the least, Scala is a general-purpose programming language developed mainly to address some of the challenges of Java language. It is used to write Apache Spark cluster computing solution. Hence, Scala has been a popular programming language in the field of data science and big data analysis, in particular.

There was a time when Scala was mandatory to work on Spark, but with the proliferation of many API endpoints approachable with other languages, this problem has been addressed. Nevertheless, it’s still the most significant and popular language for several big data tools, including Finagle. Also Scala houses amazing concurrency support, which parallelizes a whole many processes for huge data sets.

The average annual salary for a data scientist with Scala skills is $102,980.

In the end, you can never go wrong with selecting any one of the big data programming languages. All of them are equally good, productive and easy to excel on. However, Python is probably the best one to start off with.

For more updates or information on big data courses, visit DexLab Analytics.

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