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Day: November 27, 2018

M&A Consolidation in the Fintech Landscape

Posted on by Imarticus Learning

Last Updated on 3 years ago by Imarticus Learning

Fintech or financial technology is a term that can broadly apply to any kind of financial activity through digital/online means like money transfers, depositing checks through one’s smartphone, raising money for a business through the online medium, so on and so forth. Basically when one carries out any kind of financial activity only through technology and without the assistance of a person is when one has adopted the Fintech landscape.

The Growth of Fintech M&A

New businesses, even a few years back, were met by issues like inertia, funding problems and even the fear of their own obsolescence. However, now with Fintech being the “in” thing, large corporations are now beginning to view fintech startups as an opportunity to scale up what they offer and even amplify their customer experience at a faster pace in comparison to their competitors.
‘The Global Fintech Report’ by PwC published in 2017 states that “82% of financial institutions expect to increase fintech partnerships between 2018 and 2023”, thus making the fintech M&A the core for the development of the financial service sectors.

Large Corporate Opting To Acquire/Merge with the Fintech Landscape

With technology being extremely important in today’s world, large corporates across businesses are now opting to get into the M&A space in Fintech. These organizations realised that, to deal with the company’s financial needs they would need to establish calculated partnerships with lean, tech-savvy teams that would help in bringing about significant benefits to their business.
Such M&A consolidation would mean:

  • Cost-effective and speedy routes into the market with the help of the latest technology.
  • Easy access to the demographics of a new client who prefer to engage with businesses through digital channels.
  • Easy cross-selling opportunities through the digital platform.
    Mergers and Acquisitions in 2018 in the Fintech Landscape:
    2018 in fact has seen some of the big names in the corporate world merge and acquire some fintech startups to further their business need in various ways.Earlier in 2018 JLT Employee Benefits (JLT), acquired the award-winning digital saving and investment service Moola! The acquisition of Moola by JLT’s was to help UK businesses deliver better performance through the improved financial, emotional and physical wellness of its people. Basically with this acquisition JLT aimed at helping its clients and employees at better wealth management.
    Marsh the insurance broking giant invested in ‘Bought By Many’ this year (2018). With this investment Marsh aims at collaborating with insurtech firms in a bid to open distribution channels and create innovative insurance solutions for clients.
    Cake Technologies, the U.K. fintech startup was acquired by American Express as well this 2018. Cake Technologies aims at bringing about more convenience to pay restaurant or bar bills and with American Express onboard the plans are to beef up its payment options for Amex members.

    Best Performing Fintech Sectors Currently

    Lending: After the financial crisis in 2007-2008, the lending sector dried up, thus making space for a host of alternatives in the lending business. These made use of technology largely and the need for middlemen were cut off. Fintech being a part of this sector also meant greater returns.
    Investment: With banks being reluctant to increase the rate of interest on investments the fintech landscape stepped in. Fintech courses aims at solving this issue by delivering online investment platforms.
    Payments: Transferring money between accounts has also been made easy with the help of technology. It’s also less expensive as compared to the pre-fintech era!

    Conclusion:

    As the fintech M&A is picking up pace and formation of partnerships and alliances among companies and fintech is gathering momentum it is becoming increasingly important for large corporates to have a targeted M&A strategy as it is highly needed for businesses to keep up with the technological growth. On the other hand, fintechs need to carefully plan and decide with whom they would like to get into an M&A space so as to be able to grow exponentially.
    For more details, you can directly browse: https://imarticus.org/

AI, blockchain playing a key role in India’s fintech revolution: Report

Posted on by Imarticus Learning

Last Updated on 3 years ago by Imarticus Learning

Financial technology, or fintech, as it’s popularly known, has taken the finance world by storm by introducing path breaking innovations and a very novel way of banking, the world over. And recently, fintech has become the next big thing in India too. Buzzing e-commerce and product deliveries at the doorstep have risen the demand for ‘one touch’ banking that only fintech can provide. India is truly moving towards becoming a ‘knowledge-driven’, reasoning oriented and digitally empowered nation.
What facilitates fintech is a set of technologies and business models that are responsible for its growth and sustenance. Among the technologies that power fintech, Artificial Intelligence (AI) and blockchain have played a very significant role. This article discusses as to how AI and blockchain have played a central role in the fintech revolution in India, based on the joint report by the consulting giant KPMG and IT services giant NASSCOM.
In India, “Go Digital” initiative, together with the ever-growing e-commerce market, has been a significant catalyst for the fintech revolution. As India is inching towards a less-cash economy, digital payments are estimated to increase by at least ten times. Studies indicate that prepaid payment instruments registered gigantic volume growth of over 162% in the 2016-17 fiscal year. With the digitization of banking, interoperability and universal wallets are hassle-free and seamless.
The Government of India has proposed a two per cent discount in GST for users who make digital payments. This is a strategic move that will automate workflows, ensure best accounting practices,  tax compliance and a new approach towards digital inclusion. In the insurance sector too, there is an increased usage of advanced data techniques and analytics to identify risks and avert potential dangers.
Public and private sector banks are implementing Artificial Intelligence and Machine Learning methods. Take, for example, Kotak Mahindra Bank and HDFC banks have introduced AI-powered chatbots for enhanced customer experience and efficient business operations, raising the productivity by leaps and bounds, as clearly shown by the profit margins of the various quarters. Added to that, robo advisors, the first of their kind provide comprehensive and accurate information to make the best possible decisions.
A lot of iterative processes can be avoided, and customer satisfaction is much higher because they are given the exact specifications that they are looking for, without much hassle. With AI, a lot of grey areas and ambiguity can be eliminated. These are some of the many wonders that AI is capable of! Of course, AI can solve problems with efficiency and speed that are beyond the purview of the human mind. Automation is the one thing banks and fintech companies are looking to achieve, and this is mostly backed by artificial intelligence and machine learning.
When everything went digital, a large group of people expressed concern over how they prefer human interaction. Well, AI has been able to fit that requirement to a large extent. There are many bots that are now ’emotionally capable’.  One of the most significant advantages of using AI to power fintech has been the accuracy of fraud detection and pre-emptive policies based on predictive analytics. For example, based on behavioural patterns, bots can suggest users about their financial habits and suggestions for better handling of finances, all at the touch of a mobile screen. Even the most minute and seemingly insignificant data are analyzed by machine learning algorithms to form meaningful insights.
Another behemoth running the fintech revolution is the blockchain technology. Essentially, blockchain is a public ledger like a system that records transactions publicly and sequentially, for cryptocurrencies. Blockchain has been a robust system, making it much preferred for fintech. It is a formidable combination of public sharing and tight security. Aligning with fintech‘s vision, the blockchain technology empowers the user and makes for a friendly experience. With peer to peer money transactions becoming operational, blockchain has found a stable ground for itself.
The Indian government has recognized the role of blockchain technology in good governance. Andhra Pradesh has set up a Blockchain centre of excellence that has invited startups and experts to collaborate towards creating the first blockchain state in the country. Other states like Karnataka and Maharashtra are catching up fast with this trend. ‘India Chain’ of the NITI-Aayog is planning on implementing a fully enabled blockchain infrastructure to deal with e-governance, Aadhar, court cases, property records etc. On the educational front in India, several universities are providing fintech courses, replete with artificial intelligence and blockchain technologies and business models supporting the same.

Is Blockchain Worth Investing In? If So, How Do You Go About It?

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Last Updated on 3 years ago by Imarticus Learning

Cryptocurrencies drew a lot of discussion and attention with falling prices, bans and legalese. Currently riding high is the Blockchain technology supporting it worth investing in.

Blockchain Technology

Simply put, the revolutionary Blockchain technology makes possible a precise digital system that accounts all transactions while storing every change in a “block.” A series of blocks form a “chain” and its name. Further, they are locally stored but updated instantaneously on all networked computers making the system recorded, secure and manipulation-free. A huge boon, especially when financial transactions are involved.
Very large names like Google, Apple, Microsoft, IBM, the banking, fintech, and financial sectors and startups like Sia Cloud are probing, using and relying on Blockchains and hence the share values of firms invested in it is bound to grow in leaps and bounds if it becomes the next successful unicorn in today’s digital and rapidly transforming markets.

Blockchain Shares and Where Is It worth Investing In

When cryptos are not for you then investing in the technology behind them, makes perfect sense and is the right way to go. Brokers and trading are synonymous, and the field is complex. There are also free brokers like ING-Diba and Comdirect who provide a good experience in trading.
Currently, the market in Blockchain shares has two groups from the investment perspective.

Low Capital Startups and Holding Companies

  • TIO Networks provides cloud services to pay multiple channel invoices in the unbanked areas. Currently, they have three BU’s Telecom Solutions (service-provider area), Consumer Financial Solutions (B2C) and Biller and Agent Solutions (for process-payments).
  • Bitcoin Group SE the holding company focuses on disruptive, innovative business models and technologies for the cryptos and Blockchain segments. They own Bitcoin Deutschland AG.
  • The British Coinsilium Group is invested in fintech innovations and Blockchain-based technologies.
  • Digitalx and Telefonica will collaboratively make the Blockchain-based money transfer app Airpocket for payment transfers.

Large Companies Using Blockchains in Cryptocurrency and Financial Technology

  • Apple terms Zcash a legitimate crypto because it is anonymous, private and uses secure zero-knowledge Blockchain technology.
  • IBM offers ‘Blockchain Clouds’ targeting high-speed transactions of 1000/second in businesses and trading markets.
  • RWE and Innogy are also using sustainable Blockchains for their new venture.

There are many others like these which are worth researching and are using the technology for cybersecurity, the IoT, AI, cloud computing and such emerging areas. The most significant innovation would be when fintech courses are involved and include important areas like investing in internet values and potential of Blockchains conduct open discussions on cryptocurrencies investments and their future in our digital world of TODAY.

How the US Midterms Changed Fintech?

Posted on by Imarticus Learning

Last Updated on 3 years ago by Imarticus Learning

The ironic response of the US asking India to be more open and encourage international companies makes one wonder about the impact of the midterm elections in the US. 
Let’s ruminate a bit on it.

The Midterms 2018

The US midterms-2018 has split the political struggles of the legislature between the Democrats and Republicans who insist on the deregulation for rules on processing payments and creating a focus on the protection of US consumers. It even brought in changes in the committee leadership creating a political-stalemate with a wafer-thin majority. While opposing sides claim to promote innovation and technological progress, their policies are in opposition to crucial issues affecting the fintech industry.
The Democratic stance is over-sighted and could harm market-based developmental pushes, while the Republican stance is isolated and not in the interests of international cooperation or an unregulated internet. It is a no-win situation with the Democratic control of the House giving congressional action proposals which face vetoes or stalling in the Senate.

Effects on Innovation

The financial technology based e-commerce and payments across borders developers using blockchains, clouds, and mobile apps will now think twice about the US first and excesses reining in policies. Though it was proposed by Steven Mnuchin the Treasury Secretary to offer fintech startups, identity-based technology, and sandboxes regulatory relief even temporarily as in the UK, the Democrats shut it down. The administration, however, ended the policy of internet neutrality vital to technology development and innovation.
The Trump administration’s stance on immigration curtails benefits to skilled tech workers which in turn will affect the global tech-markets and America’s competitiveness. Political pressures have also affected the data protection policy and impacted fintech companies as a result. In the political chasm is the open data rule.

Learning from the Midterms

Fintech courses would do well to study both Trumpism and Brexit and their core policy of isolationism favouring domestic-development. Western payments companies like Paypal, Stripe, Mastercard, Visa, Walmart, Amazon, and others are now in a state of flux and uncertainty. The vital markets of China and India now require local-presence to make or offer payments in them. India also requires local data storage which it terms a security measure while silently promoting Paytm a local mobile payments solution. The success of the UK’s sandbox concept is affected by the Brexit’s uncertainty. Meanwhile, Lithuania emerged as a fintech hub for the fleeing fintech companies.
The net result is that sanctions and an uncertain political environment become a hurdle to both technology and market development. The US stalemate is its own foe irrespective of any political party’s vision.
 

Financial Inclusion and Fintech Use in the Industry

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Last Updated on 3 years ago by Imarticus Learning

In a bid to liberate the poor and marginalise, the Indian Government has rightly focused on creating jobs. The focus lights are on the fintech startups, the banking sector, digital payments and measures to encourage them in the hope of achieving financial stability and financial inclusion.

Importance of Fintech and Financial Technology

The winning combination of technology and finance working in harmony is now coined fintech and is crucial to all financial transactions by banks, e-commerce sites, NBFCs, payments providers, merchants, and service- providers. Every sector including banking, real estate, governmental measures and subsidies, telecom, insurance and everything in between depends on technology to crunch their numbers, maintain paperless records, KYC compliance, subscriber identification verification and a host of other supportive technological innovations.
Fintech holds immense developmental potential. It creates more jobs through its startups, sandboxes, incubators and newer companies that improve telephony, cloud services, data storage, big- data, and deep-learning capacities across the board. They, in reality, accelerate financial inclusion.

Fintech in Financial Inclusion

Fintech Start -ups have been able to revolutionise the technology backbone of the financial transactions impacting almost all sectors of our economy. Delivering better financial services to the disadvantaged and unbanked has over the last decade been able to take digitalization to the grass-root levels thereby improving financial inclusion.
The government has implemented on its part, many measures backed by fintech courses to improve the lot of the rural poor. Measures like promoting mobile telephony, e-KYC, the opening of basic zero balance savings accounts, encouraging rural banking, delivering of subsidies through Aadhar directly to beneficiaries, self-help-groups, and micro-financing, improving credit counseling centers, the Kisan Credit Card and many more. Using fintech, in little over a decade, the government has also successfully implemented its policies by bringing in internet banking, ATM machines, mobile banking, electronic instantaneous fund transfers, and many such innovative measures.
Notable among the measures for financial inclusion is:

  • Aadhaar card
  • The UPI and cashless transactions
  • Smartphones and mobile telephony
  • Zero-balance Jan-Dhan Yojana bank accounts

Suggested Policies By Fintech Courses
Cybersecurity, regulation of data protection and privacy, proper use of the Aadhar database are required and the need of the hour for financial transactions and digitization. Creditworthiness evaluation, e-KYC, online payments need to be strengthened to fulfill the urgent credit needs of all people rural or urban.
Initiating measures like sandboxes, incubators, and testers to encourage mastering the skills in Fintech is the right way to go, to a country that has no dearth of innovators or technical knowledge. India now needs to adapt and assimilate changes in technology by using strategically the full potential of the fintech advancements.
In conclusion, loopholes and gaps in policies need to be plugged for common good rather than personal vision.

How Machine Learning Helps in Psychiatric Epidemiology

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Last Updated on 3 years ago by Imarticus Learning

In India, where, as per medical surveys, every sixth child needs medical supervision for health conditions, schizophrenia is often left untreated and diagnosed. It can cause lifelong trauma and is a severely disabling illness with hallucinations, cognitive impairments, and delusions. Early diagnosis and the use of anti psychotic drugs are imperative. Predicting the course of the illness and treating it with a suitable drug is often by trial-and-error manual offline learning. That’s where the use of ML and AI in the epidemiology use in psychiatric illnesses holds immense potential and scope for growth.
Especially in our country with expensive treatment, lack of medical facilities dedicated to such mental illnesses and a huge population being rural poor being real deterrents.

ML In Predictive Analysis of Responses and Treatment

Improved MRI tools enable visualization of the smallest brain structures like sub fields in the hippo campus. The study is crucial in the treatment of psychiatric sicknesses like schizophrenia where the early recognition and assessment of the thickening or volumetric changes in these fields detected by neuroimaging can be used in morphometry and predicting cognitive declines in the pathology of the hippo campus.
AI is used in the diagnosis of schizophrenia reporting recent onset and not using treatment also known as (first-episode drug-naïve) FEDN. ML and suitable architectural frameworks help researchers evaluate and interpret these MRI scans and brain signals of the hippo campus.
The correlations of information between other cortical regions and the signals of the superior-temporal cortex got from resting-state MRIs are used by the algorithm to identify schizophrenia patients and the response to specific antipsychotic treatments very accurately.
You can now learn all about such ML, big data analytics and AI developments and uses through machine learning courses.

3d-Cnn Spatial Image-Classification

3D-CNN are convoluted neural networks used for 3D modelling, and LiDAR (light detection ranging) data classification under supervision. Cranial Imaging, occurrences of neural events and surveillance are now computer aided and should necessarily be part of Big data Hadoop training courses.

Machine Learning and Predictive Analytics

The best example is of the Alberta University study using an ML algorithm, and MRI visualized images of treated, diagnosed, untreated and healthy persons. Hippo-campus sub field volumes were used to predict responses using regression of support-vectors. The SVR-input was normalised to normal feed levels and split randomly in the module for cross-validation and datasets training in sci-kit. The prediction model and its features were accurately calculated using an inbuilt datasets training-LOOCV.
Machine learning courses in Indiainspired by the technological advancements and uses in psychiatric epidemiology are quickly adopting new content in an innovative move to use ML for predictive analysis.

Build Your Own AI Applications in a Neural Network

Posted on by Imarticus Learning

Last Updated on 3 years ago by Imarticus Learning

Today Big Data, Deep Learning, and Data Analytics are widely applied to build neural networks in almost all data-intensive industries. Machine learning courses in India offers such learning as short-term courses, MOOCs, online classrooms, regular classrooms, and even one-on-one courses. Choices are aplenty with materials, tutorials and options for training being readily available thanks to high-speed data and visualization made possible by the internet.
The study on jobs in Data Sciences says that core skills in Python are preferred by recruiters and is requisite for jobs in data analytics. The challenge lies in formulating a plan to study Python and the need of a specialist to help understand the technical and practical aspects of this cutting edge technology.

Why do a Specialization Course for Beginners?

Not all are blessed with being able to learn, update knowledge and be practically adept with the Python platform. It requires a comprehensive knowledge of machine learning, understanding of data handling, visualization techniques, AI deep learning, statistical modelling and being able to use your expertise on real-time practical examples of data sets from various industries.
Machine learning courses and case studies on Python platform are conducted in flexible learn-at-your-own-pace sessions in modes like instructor-led classroom sessions at select locations, virtual online classes led by certified trainers or even video sessions with mentoring at pre-determined convenient times.
One can do separate modules or certificate Big data Hadoop training courses with Python to understand data science analytics and then opt for modules using AI for deep learning with Python or opt for a dual specialization by doing the beginners course and courses covering AI and Deep Learning with Python. The areas of Deep Learning and AI both require prior knowledge of Deep Learning, Machine Learning, and data analytics with Python.
An example of one such course is the AnalytixLabs starter classes in Gurugram and Bangalore as a speedy boot-camp followed by a package of two courses in AI Deep Learning with Python and the Data Science with Python. The prerequisites are knowledge of at least one OOPs language and familiarity with Python. Their 36 classes, 250-hour course offers dual specialisations, and 110 hours of live training using multiple libraries in Python.
Just ensure you choose the right course to allow your career prospects to advance and allows further learning in Python-associated specialised subjects.

How AIML Can Facilitate a Holistic Digital Transformation of SMEs

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Last Updated on 3 years ago by Imarticus Learning

Using AI digitised mobility-efficient business management empowers SMEs to expand to any region globally with literally no associated monetary or infrastructural deterrents. Especially in processes like strategy-based planned sales, financial management, supply chain logistics, and marketing management where the focus should rightly be on the operational aspects rather than offline management of these which reduce enterprise efficiency.
Notable benefits of machine learning courses in India are learning better workflow management, enabling operational management to reach out, service and retain the all-important customer base. Increased cost-reduction, increased satisfaction levels of customers, doing away with time-consuming redundant offline process management and the obvious maximising of profit margins and enterprise efficiency result.

Role of Machine Learning-ML and AI

Issues are unique to every enterprise. Solutions should emerge from the workflow and be need-specific to the enterprise and its segment. Automating the logistics of the supply chain processes and sales can be optimised by ML and AI to build solutions meeting the needs and precise requirements of any business or industry with a high level of precision and customisation through the proper use of the huge data repository available with them.

Data and Challenges

Data is the backbone of automation and readily available with SME’s. Greater volumes in the database ensure tweaking for quickening and process efficiency. Big data Hadoop training courses help streamlining data, identifying and eliminating unnecessary recurrent processes and automating the process for fixed quicker and efficient outcomes is what ML, data analytics and AI intuitive combinations does when customizing processes and big data.
This indirectly frees-up the crucial time-component spent on customer interactions. ML and AI bring huge benefits in pattern recognition and predictive analysis. Their use helps deliver effective business solutions with quick outcomes by identifying and automating recurring procedures and patterns. Thus the digitization of marketing and sales drive profit and efficiency in the enterprise.

Customer Service Paradigms

In today’s scenario the pervasive use of the internet, use of digital tools, mobile apps and smart-phones create a huge database of young consumers under-35, who use and prefer digital methods to offline methods. Gainful insights are provided through their feedback, need for value-enhanced solutions, customer interaction and resolutions for customer satisfaction.
The success of SME’s depends on adapting and catering to this sector which forms nearly two-thirds of the total Indian population. Many shy away from building a digital infrastructure citing prohibitive costs involved. But, as per digital customers and a study by Google-KPMG, SMBs and SMEs have the potential to grow twice as fast with the adaptation of ML and AI.
Do we need to say anything more for machine learning courses?

Facts on Machine Learning and Statistics

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Last Updated on 3 years ago by Imarticus Learning

All machine learning courses in India need proficiency in statistics. However ML is not only statistics but definitely draws inspiration from analysis of statistics. This is so because data is their common factor. An ML-engineer though must and should have proficiency in statistics, while an ML-expert needs to only have sufficient knowledge of basic statistical techniques and data management. Let’s look into why this is so.

Overlaps of Machine Learning and Statistics

Machine learning courses of today borrow concepts like data analysis and statistical modelling to arrive at predictive models for ML. Machine Learning is a branch of computer science while statistics deals with the analysis of statistics in pure mathematics. However, they are interdependent mathematical applications both dealing with the analysis of data, data models, and problem-solving.
It goes without saying that statistics is the older sibling and yet today even statisticians use ML to achieve its end results with Big Data and for Predictive Analysis. Similarly, ML draws on statistical analysis though its aim is entirely different. That’s why Big Data Hadoop training courses also need knowledge of statistics and database management.
Mostly the overlap and confusion occur because both use algorithms and data to predict the end results. However, it is incorrect to equate the two, which are separate advanced fields, in two different branches. They are at best complementary interdependent fields which can aid each other much like siblings often do. Two separate individuals, completely different, in one environment, and with individual destinations. Sure they walk the same path at times!

Clearing the Confusion

Statistics uses a model with defined parameters fitting the data tested through classification and regression techniques to account for clustering and density estimation, to provide the best inference. ML works with networks, graphs and bar charts learning from general data through assigned weights using unsupervised learning techniques to give an accurate prediction of outcomes.
Looking very closely into the two one will notice that ML has no set rules, equations, parameters, variables or assumptions. It learns from the data input and provides a predictive outcome. In statistics, you get an inference unique to a small data set with fixed variables and based on strict regression and classification techniques of mathematical equations. Though older, statistics is pure math. ML is a carefree youngster, which uses and learns from past data, has no limit to data used or variables present and works with algorithms that govern data to give an accurate predictive outcome.
An ML Engineer and Statistician may have areas where their jobs overlap. They share a common path through the use of modelling and data and then branch out to their own destinations. Truly they are complementary in nature bring out the best in the other and helping each other achieve individual end results.

What is the Big Challenge of Shadow Banking in India today?

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Last Updated on 3 years ago by Imarticus Learning

Any investor with a semblance of market awareness would know by now about the hits that the Banking Sector has received. The NPAs truly brought to light some deep fractures in the banking practices currently, and it resulted in a reduction in the value of the stocks of many prominent banks.

However, owing to some corrective measures taken by the Reserve Bank and a change in the overarching structure, the banking sector has since been recovering from this fall.

However, these NPAs which are being addressed are only those that have been in possession of banks – the NBFCs are not included, in most cases. NBFC or Non-Banking Financial Companies are also in the same business of lending and borrowing, akin to banks in that respect. The NPAs in possession of these NBFCs also tend to have deep implications on market health and the economy.

Recently, the debt default by the IL&FS brought forth the depth of the financial risk that India’s NBFCs pose to the economy. IL&FS, or Infrastructure Leasing and Financial Services is actually a conglomerate which deals with financing and developing various infrastructure projects in the nation. This mini-crisis has worsened the capital adequacy and has contributed to the credit crunch happening today in India.

Many growing sectors were heavily dependent on NBFCs for credit, and these even include established sectors like Real Estate and the likes. Recently, a report by the Investment Banking Company Credit Suisse stated that the exposure of the NBFCs to an assortment of real estate companies could be as much as Rs. 20,000 Crores. This led to a downfall of the shares of Real Estate companies, and the economy was affected too.

The issue is that Banks is still reeling from the NPA issue that hit them head on, and are currently unable to neither increase the interest rates on their loans nor lend more to those real-estate companies that are looking for credit. This led to NBFCs taking the mantle, and this phenomenon is known as ‘shadow banking’. In India, the shadow banking sector of the lending agenies appears to be larger than that of the banks, which can have dire consequences in the economy of the nation too.

If you are interested in understanding more about the issue and about investment in general, take a look at the investment banking courses available on Imarticus Learning!

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