Here are the top 5 PHP frameworks that every developer should learn:

1. Laravel

Laravel is a powerful, full-stack PHP framework with a great community and a focus on elegance and simplicity. It is a popular choice for web development projects of all sizes, from small blogs to large enterprise applications. Laravel is known for its expressive syntax, powerful features, and large ecosystem of libraries and packages.

2. Symfony

Symfony is another popular PHP framework with a strong focus on modularity and flexibility. It is the foundation for many other PHP frameworks, including Drupal and Magento. Symfony is a good choice for developers who need a framework that is highly customizable and can be adapted to a wide range of projects.

3. Yii

Yii is a high-performance PHP framework that is known for its speed and scalability. It is a good choice for developers who need to build web applications that can handle a lot of traffic. Yii is also a good choice for developers who are looking for a framework with a large and active community.

4. CodeIgniter

CodeIgniter is a lightweight PHP framework that is easy to learn and use. It is a good choice for developers who are just starting out with PHP or who need to build a simple web application quickly. CodeIgniter is also a good choice for developers who are looking for a framework with a small footprint.

5. Slim

Slim is a micro-framework that is designed to be as small and simple as possible. It is a good choice for developers who need to build small, RESTful APIs or web applications. Slim is also a good choice for developers who are looking for a framework that is easy to learn and use.

These are just a few of the many great PHP frameworks that are available. The best framework for you will depend on your specific needs and preferences.

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In today’s competitive job market, having the right certifications can give you a significant edge over other candidates. The top 10 certifications in the world are those that are in high demand and offer a good return on investment. They can help you advance your career, earn a higher salary, and gain access to more job opportunities.

Here are the top 10 certifications in the world:

1. DevOps Certified Professional (DCP): A certification that validates an individual’s knowledge and skills in implementing DevOps practices and principles, including collaboration, automation, and continuous integration/continuous delivery (CI/CD).

2. DevSecOps Certified Professional (DSOCP): This certification focuses on integrating security practices and principles into the DevOps workflow. It covers areas such as vulnerability management, threat modeling, and secure coding.

3. Site Reliability Engineering Certified Professional (SRECP): SRECP certification validates an individual’s expertise in implementing the principles and practices of Site Reliability Engineering (SRE). SRE focuses on achieving reliability and operational stability of systems through a combination of software engineering and operations.

4. AIOps Certified Professional: This certification pertains to the implementation of Artificial Intelligence for IT Operations (AIOps). It encompasses using AI and machine learning techniques to automate and enhance various IT operations processes, such as monitoring, event correlation, and incident management.

5. DataOps Certified Professional: DataOps certification focuses on the principles and practices of DataOps, which involves streamlining and optimizing the entire data lifecycle, from data ingestion to analytics and insights. It emphasizes collaboration, automation, and data quality.

6. MLOps Certified Professional: MLOps certification covers the implementation of Machine Learning Operations (MLOps) practices, which aim to streamline and optimize the deployment and management of machine learning models within the software development lifecycle.

7. BizDevOps Certified Professional: BizDevOps certification combines business (biz), development (dev), and operations (ops) practices, emphasizing cross-functional collaboration and alignment. It validates an individual’s ability to integrate business requirements into the DevOps workflow.

8. CloudOps Certified Professional: This certification focuses on the implementation of cloud operations practices and principles. It covers topics such as cloud architecture, deployment, scalability, and cost optimization.

9. ITOps Certified Professional: ITOps certification validates expertise in traditional IT operations practices and principles. It encompasses areas such as IT infrastructure management, system administration, and incident response.

10. SysOps Certified Professional: SysOps certification focuses on system administration and operations within the context of cloud computing environments. It covers topics such as resource provisioning, monitoring, and troubleshooting in cloud-based systems.

In conclusion, certifications have become an essential aspect of advancing in one’s career. The article highlights the top 10 certifications that span various domains, greatly augmenting your professional opportunities. Whether your interests lie in DevOps, security, AI, data, or project management, there’s a certification that can distinguish you from others. Seize the opportunity and invest in your future by embarking on the pursuit of one of these certifications today!

To gain further insights into these certifications and initiate your path towards professional excellence, visit the DevOps School Certification Page. Discover the array of courses, resources, and prospects that these certifications provide.

The post World’s Top 10 IT Certifications in 2023 appeared first on Artificial Intelligence.

To build a successful career in the field of artificial intelligence, the following skills are required.

Artificial intelligence has become the most popular sector in the tech industry at present. It is also a very popular choice of career for aspiring students. The following are the essential requirements that the aspirants must hone to make a successful career in artificial intelligence.

Educational Requirements

To build a career in artificial intelligence, students must acquire a bachelor’s degree in computer science, information technology, mathematics, statistics, finance, or economics. They should also be proficient in analytical skills, problem-solving skills, creative thinking, effective communication, and industry knowledge. A Master’s degree in advanced computer science education with a specialization in artificial intelligence or a Master’s degree in artificial intelligence will allow the students to weave their AI career. A working professional from the IT industry can build their skills in practical as well as technical knowledge regarding AI.

Technical Skills

Students aspiring for an AI career should learn programming languages like R, Python, Java, and C++. They should have a knack for coding. This will help them understand concepts like data structures and classes. Learning more than one programming language will always prove to be an added advantage since organizations these days are focusing on individuals with multiple skill-sets. Statistics, Linear Algebra, and Calculus would help them understand how algorithms and machine learning would function. A solid understanding of probability and models like Naive Bayes, Hidden Markov models, and Gaussian Mixture models are required. Familiarity with vectors, matrices, matrix multiplication, and understanding of integrals and derivatives are also advisable to be acquired for an aspiring AI engineer. They should also know more about neural network architectures, which have a vital role to play in performing acts of speech recognition, image classification, and translation.

Non-Technical Skills

Building an AI career does not only require technical skills but an AI engineer must also have the ability to explain matters in a simple way to people of the industry. While discussing the analytics with company people, using technical languages will not be helpful as everyone is not familiar with them. So, the AI engineer will be required to explain critical matters to the stakeholders and industrialists. This is why non-technical skills are very important.  In-depth knowledge regarding the industry scenario will also prove beneficial for AI professionals working in this field since this will help them understand the real-world scenario and the main issues of the industry.

Critical Thinking

After acquiring technical and non-technical skills, AI aspirants must develop a bridge between those skills. After creating structured smart data, AI engineers should come up with insightful conclusions from those findings. With the emergence of AI, every sector is now looking to implement artificial intelligence making the demand for professionals a lot higher.

An amalgamation of all these skills would make the most successful career in the field of AI. Posts like big data engineers, program developers, and data analysts have high salaries and come with a lot of responsibility. The most important matter is that without a knack for coding and programming language, building a career in this field is almost impossible.

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Meanwhile, India shows evidence of evolving gender dynamics as women adopt online learning at one of the fastest paces globally. The share of overall Coursera course enrolments as well as in STEM courses from women learners in India saw an unprecedented increase. 

Despite high skills proficiency in areas like Machine Learning and Math, India trails in data skills, as per a report by online learning platform Coursera. India ranks 55 in business, at 66 in both technology and data science. In Asia, India is placed at 16, ahead of countries such as the Philippines and Thailand, however below others like Singapore and Japan.

As per Coursera’s Global Skills Report 2021, overall, India ranks 67th globally. Despite the increase in enrolments, India continues to witness a digital skills gap, the report reveals. The study draws on performance data since the pandemic’s onset from more than 77 million learners on the platform to benchmark skills proficiency across business, technology, and data science for over 100 countries.

A report by AWS estimates that digitally skilled workers represent only 12 per cent of India’s workforce, and the number of employees requiring digital skills is expected to increase nine-fold by 2025.

WEF and PwC reveal that national upskilling initiatives could unlock India’s potential to achieve a much faster pace of economic growth and improve the employment rate. However, workforce displacement due to the subsequent waves of the pandemic is compounding the existing gap.

“The pace of skills transformation is slower than the pace of digital transformation in India, as is the case in several countries across the world. Learners must invest in both soft and technical skills to prepare for jobs of the future,” said Raghav Gupta, Managing Director – India and APAC, Coursera.

Cloud Computing is India’s strongest technology skill with 83 per cent proficiency. A report by Wheebox, ranks India among the top 9 nations for global public cloud services, with a growth rate higher than the global average.

As demand for data scientists outstrips supply worldwide, India is also grappling with a shortage of data science professionals. Coursera’s GSR 2021 indicates that Indians have 52 per cent proficiency in ML & 54 per cent in mathematical skills. However, there is significant room for improvement in data analysis and statistical programming, ranked only at 25 per cent and 15 per cent skills proficiency respectively.

Meanwhile, India shows evidence of evolving gender dynamics as women adopt online learning at one of the fastest paces globally. The share of overall Coursera course enrolments as well as in STEM courses from women learners in India saw an unprecedented increase. 

Based on the performance data of millions of learners on Coursera globally, the report also reveals the skills and time required to prepare for entry-level roles.

Recent graduates and mid-career changers can develop entry-level, digital job skills in as little as 35 to 70 hours (or 1-2 months with 10 learning hours per week). On the other hand, someone with no degree or technology experience can be job-ready in 80 to 240 hours (or 2-6 months with 10 learning hours per week).

The most transferable skills across all future jobs are in human skills like problem solving and communication, computer literacy, and career management.  

With 77 million learners, 6,000 institutions, and more than 5,000 courses from the world’s leading universities and industry educators, Coursera has one of the largest data sets for identifying and measuring skill trends. This year’s report is further enriched by the pandemic-driven trends, including 30 million new learners who joined the platform in 2020.

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Big data skills are crucial to land up data engineering job roles. From designing, creating, building, and maintaining data pipelines to collating raw data from various sources and ensuring performance optimization, data engineering professionals carry a plethora of tasks. They are expected to know about big data frameworks, databases, building data infrastructure, containers, and more. It is also important that they have hands-on exposure to tools such as Scala, Hadoop, HPCC, Storm, Cloudera, Rapidminer, SPSS, SAS, Excel, R, Python, Docker, Kubernetes, MapReduce, Pig, and to name a few.

Here, we list some of the important skills that one should possess to build a successful career in big data.

1. Database Tools

Storing, organizing, and managing huge data volumes is critical for data engineering job roles, and therefore a deep understanding of database design & architecture is crucial. The two types of databases commonly used are structure query language (SQL) based, and NoSQL-based. While SQL-based databases such as MySQL and PL/SQL are used to store structured data, NoSQL technologies such as Cassandra, MongoDB, and others can store large volumes of structured, semi-structured & unstructured data as per application requirements.

2. Data Transformation Tools

Big data is present in raw format and cannot be used directly. It needs to be converted to a consumable format based on the use case to process it. Data transformation can be simple or complex depending on the data sources, formats, and required output. Some of the data transformation tools are Hevo Data, Matillion, Talend, Pentaho Data Integration, InfoSphere DataStage, and more.

3. Data Ingestion Tools

Data ingestion is one of the essential parts of big data skills and is the process of moving data from one or more sources to a destination where it could be analyzed. As the amount and formats of data increase, data ingestion becomes more complex, requiring the professionals to know data ingestion tools and APIs to prioritize data sources, validate them, and dispatch data to ensure an effective ingestion process. Some of the data ingestion tools to know are Apache Kafka, Apache Storm, Apache Flume, Apache Sqoop, Wavefront, and more.

4. Data Mining Tools

Another important skill to handle big data is data mining which involves extracting vital information to find patterns in large data sets and prepare them for analysis. Data mining helps in carrying out data classification and predictions. Some of the data mining tools that big data professionals must have hands-on are Apache Mahout, KNIME, Rapid Miner, Weka, and more.

5. Data Warehousing and ETL Tools

Data warehouse and ETL help companies leverage big data in a meaningful manner. It streamlines data that comes from heterogeneous sources. ETL or Extract Transform Load takes data from multiple sources, converts it for analysis, and loads that data into the warehouse. Some of the popular ETL tools are Talend, Informatica PowerCenter, AWS Glue, Stitch, and more.

Also Read: 5 Tips for Preparing Resume for a Data Engineering Interview

6. Real-time Processing Frameworks

Processing the data generated in real-time is essential to generate quick insights to act upon. Apache Spark is most popularly used as a distributed real-time processing framework to carry data processing. Some of the other frameworks to know are Hadoop, Apache Storm, Flink, and more.

7. Data Buffering Tools

With increasing data volumes, data buffering has become a crucial driver to speed up the processing power of data. Essentially, a data buffer is an area that temporarily stores data while moving from one place to another. Data buffering becomes important in cases where streaming data is continuously generated from thousands of data sources. Commonly used tools for data buffering are Kinesis, Redis Cache, GCP Pub/Sub, etc.

8. Machine Learning Skills

Integrating machine learning into big data processing can accelerate the process by uncovering trends and patterns. Using machine learning algorithms can categorize the incoming data, recognize patterns and translate data into insights. Understanding machine learning requires a strong foundation in mathematics and statistics. Knowledge of tools such as SAS, SPSS, R, etc. can help in developing these skills.

9. Cloud Computing Tools

Setting up the cloud to store and ensure the high availability of data is one of the key tasks of big data teams. It, therefore, becomes an essential skill to acquire while working with big data. Companies work with hybrid, public or in-house cloud infrastructure based on the data storage requirements. Some of the popular cloud platforms to know are AWS, Azure, GCP, OpenStack, Openshift, and more.

10. Data Visualization Skills

Big data professionals work with visualization tools in and out. It is required to present the insights and learnings generated in a consumable format for the end-users. Some of the popularly used visualization tools that can be learned are Tableau, Qlik, Tibco Spotfire, Plotly, and more.

The best way to learn these data engineering skills is to get certifications and get hands-on practice by exploring new data sets and integrating them into real-life use cases. Good luck learning them!

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If you want to work in data science in finance, there are a few things you should probably know: firstly, that advantages are increasingly conferred in real time by huge unstructured data sets derived from social media, and secondly that while the building blocks of data strategy (data storage and administration) are increasingly, commoditized there are still interesting roles for data scientists at the top of the “data stack.”

“Data engineering teams are really shifting their focus from low level database storage and administration and are instead focusing much more on the high value-add part of the data chain,” said Tom Taylor, head of alpha technology at fund Man Numeric, speaking at this week’s AI & Data Science in Trading conference. 

Alternative data like credit card transactions and brand sentiment tracking has now become the norm in finance, said Taylor, and there’s so much data around that to be successful funds don’t just need a team of researchers analyzing its meaning, but an “industrial scale data onboarding and data science capability.”

Some funds, like Two Sigma, have outsourced this process of cleaning data so that it can be onboarded into their systems easily, and companies like Crux Informatics (used by Two Sigma) are emerging as specialists in so-called ‘data wrangling’ – ingesting, cleaning and structuring data sets. 

Important as it is, however, data wrangling isn’t where the most appealing data jobs are. If you want to work in some of the most interesting and highest value-adding data jobs in finance, Taylor suggests you should position yourself towards the top of the “data stack.”

At Man Numeric, Taylor said the data stack looks like this. The highest value positions are at the top, the lowest are at the bottom.

‘Data stack,’ Man Numeric:

While you might be able to get a data job in finance if you’re an expert in SQL, kafka or kubernetes, therefore, you won’t get the best data jobs just by knowing about data storage and computation packages. Nor will knowing about machine learning or open source Python libraries (Man is a Python house) be the deciding factors. – The best data science jobs on the buy-side now go to people who can make data available to the rest of the organization, said Taylor.

The future is about increasing the “velocity of data” in firms, and this means enabling “self-service data,” he said. While data ingestion and storage are important, they’re at the commoditized end of the stack. The real focus is now on allowing, “users from across the organization to manipulate and analyze data.” 

This means that the new most valuable data scientists are those who can build custom dashboards or visualizations that allow colleagues to access data directly. – Data science teams have an “empowering and enabling role.” It’s not just data science teams who will do data science. Taylor said that data fluency across organizations is increasing, both as existing staff add to their skills and as new data-confident graduates are hired. 

If you want a long term, remunerative career in data science in finance, you therefore need to be pitching yourself at the top of the chart above. “If you can build self-service tools, you will be ready for the next few years,” Taylor said.

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While deep learning is driving advances in artificial intelligence that are changing our world, data engineering is the foundation for the new world of Big Data.

Udacity, a silicon-valley-based global lifelong learning platform, has released the list of the most popular Nanodegree programs in India in 2020. The data is based on the number of enrollments during the year, showing the demand across different states and union territories.

Deep learning and data engineering are top Nanodegree programs showing the country’s growing interest in AI and data. While deep learning is driving advances in artificial intelligence that are changing our world, data engineering is the foundation for the new world of Big Data.

There is no doubt about the fact that COVID-19 has changed the global job landscape. This can be clearly seen in how the demand for some of the Nanodegree programs shifted in 2020 vis-a-vis 2019. Take a look:

There has always been huge traction amongst learners to upskill themselves on the tech front. In the wake of the pandemic, rapid digital adoption has further made advanced courses more desirable worldwide for forward-looking professionals. The findings reinstate that the demand for technology-oriented jobs is continually growing and many regions have also been able to leverage technology to upskill themselves for futuristic job profiles.

Here are some key findings:

Deep learning is big in Karnataka

Karnataka holds the lion’s share for maximum Nanodegree programs. As much as 24% demand for Deep Learning and 34% of the total demand for Data Engineering Nanodegree programs comes from this state.  The demand for AI Product Manager (38%) and Product Manager (60%) is also the highest in the state. 

Maharashtra digs on data

Data Science and Deep Learning are the most popular Nanodegree programs in Maharashtra. The Product Manager Nanodegree program is popular in Maharashtra. More than 40% of the enrollments come from this state. It is also a popular destination for data science Nanodegree program and Machine Learning Engineer Nanodegree program with 25% and 21% enrollments coming from these regions respectively.

Delhi is all about data & programming

The Indian capital, Delhi, is a frontrunner in the mainstream programming languages. It drew 22% and 23% demand for C++ and Full Stack Web Developer. The Data Analyst Nanodegree program is also big in the region with 21% enrollments coming from the National Capital Region.

Telangana produces more developers

The state of Telangana racked in 16% of enrollments for the Full Stack Web Development Nanodegree program and C++ Nanodegree programs. The Machine Learning Nanodegree program is also big in the state with around 11% enrollments coming from it.

More than half of self-driving car engineers come from Tamil Nadu

Tamil Nadu produced more than 50% Self-Driving car engineers in 2020 in India. Computer Vision and Machine Learning Nanodegree programs are also popular in the state.

The post Deep Learning & Data Engineering Are Top In-demand Skills Of 2020 In India: Reports appeared first on Artificial Intelligence.

The University of South Carolina’s Office of Innovation, Partnerships, and Economic Engagement (OIPEE), in partnership with IBM and the USC/Columbia Technology Incubator, is offering the latest iteration of the IBM Skills Academy with a course in Data Science.

Participants who complete the course will earn a digital badge that reflects an ability to use advanced data science methods and tools to implement unique data models that can solve challenging problems across all industries. The course is designed so that participants can benefit and apply lessons to whatever career field they choose to pursue.

Last summer, UofSC hosted its first IBM Skills Academy course in Artificial Intelligence and graduated its first group of students. This new practitioner-level course in Data Science builds off that success and will teach participants how to extract knowledge from massive amounts of data, using methods such as statistics, machine learning, data mining, and predictive analytics. No matter the field of study or career path, a working knowledge of data science can help students, workers and organizations solve problems and gain a competitive advantage.

“Our mission is to help close the gap between academia and industry by leveraging our partnerships to give our students and graduates the necessary skills to be highly productive employees on day one,” said OIPEE Executive Director Bill Kirkland. “This goes for the community as well as the state’s flagship university, UofSC aspires to take the feedback we receive from industry and help build up the state’s workforce that can fill the demand for high-paying jobs, both now and in the future.”

OIPEE will host informational webinars. Register online for the Wednesday (Feb. 3) webinar or for the Feb. 9 webinar. Both informational sessions begin at 5:30 p.m.  The course begins Feb. 16, also at 5:30pm. The Data Science course will be taught by faculty from the College of Engineering and Computing and will explore concepts, relevant technologies and industry use cases. Classes will run twice a week and will include a mix of lectures, videos of subject matter experts, and interactive labs.

About IBM Skills Academy at UofSC

IBM Skills Academy is a comprehensive training and badging program designed to empower people of various backgrounds with the skills needed to excel in the job market.  Through a blended learning approach, participants learn at their own pace that best suits their individual learning style.  Once students pass the final exam, they will earn an IBM Digital Badge — a digital recognition of skills which can be shared on social and professional networks.  The badges — verified and administered by Pearson VUE Acclaim — can also be part of users’ digital signature and feature on networking platforms like LinkedIn. Future courses will be offered in Cybersecurity, Blockchain, Artificial Intelligence, Internet of Things (IoT), Cloud Computing, and Design Thinking.

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What Are The Top Skills You Need To Amass To Become A Data Scientist?

The interdisciplinary field of data science is growing with extraordinary relevance and so do data scientists. At the same time, the world is generating more data than ever before, supported by inexpensive and endless cloud computing resources available to process that data. Since data is the new currency, companies focus on extracting value from the data pool that will help them boost business and adapt to the changing technologies in the market. For this, they need to hire the right people with reliable data science skills. Data scientists are generally believed to have profound knowledge and expertise in fields like machine learning, statistics, mathematics, computing science, data visualization, and communication. Besides, it is justified to possess such technical skills as a data scientist is one of the highest paying jobs in the Tech community. Along with these, a data scientist must have the ability to solve business problems, be agile, carry effective business communication, be a good data storyteller, and a team player.

Male Dominance

A study was recently carried to observe how an individual becomes a qualified applicant for a data scientist position. The study, “1,001 data scientist LinkedIn profiles,” was held for the third consecutive year. This time, it was able to delineate the typical traits of data science professionals in 2020 and compared this data with the 2018 and 2019 figures. The study found that a data scientist’s collective image is viewed as a male (71%) who is bilingual and has been in the workforce for 8.5 years (3.5 years of being a data scientist). A data scientist works with Python and/or R and has a Master’s degree. This is because a majority of data scientists in the research are male. The proportion remained very stable — 70%-30% in 2018, 69%-31% in 2019, and 71%-29% in 2020 — and is likely a true representation of the workplace’s actual situation. It was also discovered that the median work experience of people who work as data scientists jumped from 4.5 years in 2018 to 8.5 years in 2020.

Python Preference

A data scientist’s job includes data mining using APIs or building ETL pipelines, data cleaning using programming languages like R or Python. The study noted that the most popular coding language in the field is Python. The top 5 mainly used programming languages by data scientists for their projects, i.e., Python (73%), R (56%), SQL (51%), MATLAB (20%), and Java (16%). This is a major shift from the previous year’s observations. In 2018, Python and R had the same level of adoption, which was 53%. The following year, in 2019, Python came in the lead with 54% compared to 45% for R. Now Python has established itself as the industry’s coding language of choice, with a significant lead over R.

Educational Background

In terms of Academia, the large majority (95%) of current data scientists have a Bachelor’s degree or higher. Out of those, 53% hold a Master’s degree, and 26% – a Ph.D. The study concludes that a person must aim for a second-cycle academic degree; although, having a Bachelor’s can still serve as a pre-requisite as long as the person has the technical skills and preparation required.

Next insight in the educational background was, while 19 out of 20 data scientists have a university degree, 55% of the data scientists in the cohort come from one of three university backgrounds: Data Science and Analysis (21%), Computer Science (18%), and Statistics and Mathematics (16%). There are fewer representatives of Economics and Social Sciences (12%), Engineering (11%), and Natural Sciences (11%). All of these are technical courses that prepare graduates for the quantitative and analytical aspects of the job. But a Data Science, Computer Science, or Statistics and Mathematics degree offer the best chance for a data scientist career.

Previous Job Experiences

The study also examined data scientists’ previous job occupation 1 and 2 jobs ago. Two positions prior to their current role, the average data scientist in the data pool were either already a Data Scientist (29%), an Analyst (17%), or in Academia (12%). And before entering their current role, the figures are 52% for Data Scientists, 11% for Analysts, and 8% for Academia. Apart from that, having an internship has helped people find jobs in data science. As per the study, 11% of data scientists were interns two jobs ago, and 7% of them were interns immediately before becoming data scientists.

The post UNDERSTANDING THE PRE-REQUISITES FOR A CAREER IN DATA SCIENCE appeared first on Artificial Intelligence.

Traditional financial institutions have thus far treated rural customers as a monolithic whole. This is one reason rural customers often prefer informal providers or remain excluded from formal financial services. Significant advances in technological innovations coupled with the availability of granular data at individual levels, imply that the needs of a large rural population can now be analysed better to provide customized solutions, factoring-in and respecting rural customers’ heterogeneity.

Despite misgivings of usury, for generations the ubiquitous moneylenders or sahukars have continued to be patronized by communities and are hard to dislodge, for rural credit across India. Moneylenders play an important role in rural communities mainly because of the flexibility and choice they offer customers: a) doorstep service b) little or easy documentation c) instant approval d) guarantee that one’s application for a loan will not be rejected e) often no guarantor requirement  f) loan amount and repayment schedule tailored to customer cash flows g) low prejudice around past borrowing history. This does not diminish the sometimes-catastrophic downsides customers incur, since the informal provider will not hesitate to use muscle or act outside the law. 

In contrast, for a formal financial institution with structural issues such as centralisation, inadequate local autonomy, rigid policies and products, resource constraints and other factors, there is very little flexibility in decision making. This leads to significant formal financial exclusion for rural customers. 

AI can help to change this for formal providers. 

To compete in the rural context, financial institutions need to offer customized solutions to individuals addressing the heterogeneity of the rural population, while minimizing risks and keeping costs low – imperatives to remain profitable. This is where emerging technologies such as Artificial Intelligence (AI) can help. AI and Machine Learning (ML), along with other techniques such as Natural Language Processing (NLP), robotics, and predictive analytics can be used to design customized, flexible, cost effective solutions for rural populations that are financially excluded and are often clubbed with urban customers.

For example, a fintech providing last mile distribution services across remote rural geographies was plagued by low first attempt resolution of routine queries from field banking agents. Simple queries, such as a password reset, when not addressed instantaneously lead to lost sales and commissions. To address this, the company developed a menu-based ‘smart-bot’ app designed specifically for low- literacy settings to automatically handle first pass queries. On deployment, first attempt resolution went up by a whopping 60%. Call volumes for quick resolution queries at the call centre also reduced and they were able to dramatically shift agents from a costly human call centre to a digital channel to self-service their needs. 

Similarly, people with low-incomes are far more likely to be credit invisible and generate data-points that cannot be scored through normal appraisal processes. In India, that’s more than 700 million people. A leading Indian fintech is offering a simple and intuitive solution to help micro-borrowers establish financial identities so that they can qualify for loans from formal financial institutions. The app, called Pehchaan will help people without a formal credit history and in resource poor settings to build their financial identity.  The app can also help lending institutions identify creditworthy borrowers, customize their product suite to meet the varying needs of the micro borrowers and mitigate credit risk due to un-scorable or invisible borrowers.

As illustrated in the example cases above, AI can deliver business insights against large diverse data sets in a real-time and cost-effective manner. It can offer personalised experiences and build agent loyalty. It can help increase customer conversion and open new markets for financial institutions. Use of AI based chatbots with the ability of Natural Language Processing can encourage greater engagement of rural customers.

Building an AI/ML model requires a clear definition of the problem statement, availability of sufficient quantity of contextually specific training data, decisions on whether the model needs to perform real time and contingent data storage implications. For example, the chatbot in the example described previously only required one week’s worth of calls from agents to be trained to handle the quick resolution queries at 60% higher efficiency. However, an AI fraud engine started out at ten thousand records and went up to one hundred thousand records before meeting reliable performance goals. These factors influence the levels of investment and the organization’s business case for AI.

Initiatives by the government of India towards financial inclusion, coupled with disruptive innovations in the fintech space have started showing positive results.  Almost all the income groups in rural India have benefited from new government schemes that address issues related to income inequality and growing disparity between urban and rural India. However, financial institutions still have not been able to effectively navigate the heterogeneity of rural markets due to technological and infrastructural bottlenecks. We are witnessing trends towards increased heterogeneity and deeper basic financial inclusion. The role of authentic and un-siloed rural data, and disruptive next- gen technologies such as artificial intelligence can’t be stressed enough as the enablers for a massive transformation toward a more inclusive India. By using data, AI, ML and NLP judiciously, financial institutions can create personalized offerings for rural individuals and have a viable business model at the same time.

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