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Day: August 5, 2021

How Is Investment Banking Distinguished From Commercial Banking?

Posted on by Imarticus

Investment banks help corporations, companies, and governments raise capital, assist in mergers and acquisitions, and provide ancillary services such as derivatives trading, foreign exchange, equity securities, and making markets. They act as intermediaries between buyers and sellers of stock as well. Investment banking training is extensive with the aim of having an in-depth knowledge of financial markets and concepts.

Commercial banks, or retail banks, on the other hand, are set up to deal with commercial transactions like legally taking money from individuals and corporations in the form of accounts and then lending the capital to others with interest.

Any investment banking course providing investment banking certifications will explain that an investment banker deals with companies as a financial intermediary and provides investment and financial advisory services. A commercial or retail bank provides banking services to the general public. Since a commercial bank works with the public, their customer base is much larger than that of investment banks who only works with companies.

One of the key differences between these two types of banking industries lies in the relationship and services offered by both. A commercial bank will only provide standardized services while an investment bank will personalize their services and give customer specific advice. Commercial banks are highly regulated by the government and Reserve Bank, making them more risk averse while investment banks have a higher tolerance for risk since they have lesser regulations. This makes the rewards for investment banking higher. Conversely, the losses in investment banking can be much more substantial.

The role of an investment bank is to identify companies that will benefit from their services and then make presentation to them. For example, with Amazon looking to increase its footprint across sectors, an investment bank can identify other, smaller companies who already have existing inroads in that sector, and pitch to Amazon to try to acquire the smaller company. This was seen with Amazon buying Whole Foods.

Another way investment banks work is by identifying companies that require extra capital investment in order to increase their growth. While these companies might be considering traditional commercial banks for a loan, they might be deterred by the high-interest rates. An investment banker can pitch different ideas, mainly the creation of equity or releasing bonds, to the company. The investment bank is also responsible for drawing up the legal documents for such processes. They make money by charging a commission or a fee. Sometimes, they might even buy some bonds or stock in the company.

A commercial bank provides services that are associated with traditional banking systems for individuals. They will help you procure a loan against interest with collateral or help you open and create various accounts such as RD, FD, current deposits, savings accounts, and more. These services are already pre-defined and do not change from person to person.

There are many banks that proved both forms of banking. For example, it is not uncommon to see banks help a company set up and sell an IPO and then use the commercial side to provide a line of credit to the company. This will ensure that the company enjoys quick financial growth and in turn, increase its stock prices.

What Is The Role of Machine Learning In Financial Fraud Prevention?

Posted on by Imarticus

What is the role of machine learning in financial fraud prevention?

The instances of fraud rose between 2015 and 2018, there was also an increase in the total value and volume of fraud, according to KPMG. Needless to say, financial fraud is alive and well, despite increasingly stringent measures to reduce numbers and tighten security.

Out of all fraud instances, banks reported that cyber-attacks were the most imminent risks, no doubt due to the burgeoning use of technology within the sector without adequate security. The solution is a tad ironic- to fight technology with more technology, like fighting fire with fire. Financial fraud prevention is best countered by machine learning, a subset of Artificial Intelligence.

Machine learning may be an alien concept to many, but it plays an intrinsic role in our daily functioning– even more so as times become for futuristic. Detection of email spam, product recommendations on your favorite video network or even image recognition algorithms on cellphones are examples of machine learning in everyday use.

For the banking industry, machine learning comes off much like a savior, especially in light of the many disadvantages of traditional fraud detection.

Traditional methods were heavily based on rules, which meant they could be inefficient, erroneous or hard to scale. This formed a weak buttress against sophisticated hackers who are well-versed in enhanced fraudulent methods and was as good as having no firewall after a point.

Against all of this, machine learning has an important role to play, as any Machine Learning course would tell you. Here are the benefits of using machine learning for financial fraud prevention:

Scalable, cost-effective algorithms

The benefit of machine learning algorithms is that more data means more precision. This is a definite step-up from traditional methods where bigger datasets could lead to incorrect data or even crashes. In machine learning, the algorithm learns with more data as it picks out outliers, nuances and different patterns to provide more accurate results in a more efficient manner.

It’s cost-effective, too– rule-based methods required a lot more in terms of costs to scale, whereas machine learning setups need a few tweaks to get back up and running after an update in datasets. An additional benefit is that machine learning algorithms can repeat menial, frustrating tasks 24/7 without a hitch– a feat highly impossible if a human employee were involved.

Real-time processing

In traditional methods of fraud detection and prevention, the focus was more on long-term processing, which often led to delays in resolving fraud reports and transactions. That naturally led to a lot of unhappy customers, but Machine Learning can successfully put an end to that saga.

The results from machine learning algorithms are real-time, which means instant updates and immediate results. Not only does this lead to quicker fraud resolutions, but it also helps banks identify loopholes in their system and fix them immediately.

Reduce time and increase accuracy

The main draws of machine learning are its ability to reduce the time and effort taken to detect fraud and prevent it. There are claims that a fully-ready machine learning algorithm can detect up to a whopping 95% of fraud, that too accurately. According to another Capgemini report, fraud detection software that uses machine learning can reduce the time taken to investigate by 75%, all the while improving the accuracy of fraud detection by 90%. Needless to say, machine learning purports a lot of benefits when used in the banking sector for fraud detection.

Applicable across industries

The banking sector liaises with multiple other industries, as do their customers. However, the fraud cases they deal with are often dissimilar in intensity, method, uses, and reasoning. It only makes sense, then, that a fraud detection setup is capable of handling fraud detection and resolution across all industries.

With machine learning, algorithms can be scaled or adapted for different industries including e-commerce, medicine, hospitality, retail, and insurance. By using unique datasets for each sector, similar machine learning algorithms can be used to cater to each, instead of limiting all claims to one or a few traditional fraud prevention methods.

The financial and banking sector has a lot to benefit from the use of machine learning in fraud detection– today’s machine learning courses today cover these uses in-depth, making aspirants ready for changing technology in a traditional industry.

How Machine Learning Can Improve Customer Service

Posted on by Imarticus

BPO means “business measure re-appropriating.” to put it plainly, it’s a business practice we see carried out when an association chooses to re-appropriate exercises like finance, HR, charging, and client support.

The best illustration of this is client support since we as a whole have encountered talking with somebody from an alternate nation when we’ve called a bank or objected to a Visa and required it settled.

We won’t invest any more energy examining BPO, yet our innovation discussion in this article will be centered around further developing client support. Presently, review an occurrence when you called your charge card organization. You were reasonably approached to squeeze 1 for English, press 2 for Spanish and afterward, a few alternatives were introduced before you at long last get a choice to press a number to converse with a genuine human.

Next came the check interaction where you needed to give your first and last name, then, at that point your date of birth, then, at that point your mysterious answer, or pin, or perhaps the last four digits of your federal retirement aide number. At last, a CSR (client support specialist) approves your personality and you have a chance to pose inquiries. Now, the client care specialist may have full admittance to your considered history and whatever other collaborations that you had with them before.

So what’s the job of AI in this?

Presently, envision a shrewd framework where you are consequently diverted to a savvy specialist (or a computerized specialist) who can say for sure that you are bringing in to converse with a client specialist since you were on the site or application searching for answers to a specific inquiry.

You even connected with the chatbot, yet your inquiry was not replied to. Your calling number and voice can be utilized to confirm your personality to look through your record as opposed to investing the energy to look into your data. There are machines behind the scene ingesting, handling, and examining this collaboration continuously and anticipating that you are going to call the client support.

AI (ML) takes the client contact point, tracks the action progressively, and predicts the following best activity dependent on client action. AI predicts client future necessities dependent on the set of experiences which results in up-selling and strategically pitching openings.

The framework even triggers hyper-customized notices to CSR to impart to the client while the client is as yet on the call like new items or administration offering since this client looked for that specific watchword before.

This is the only one-way organizations can utilize ML to further develop client support. Here are a couple of alternate ways you can use ML to further develop the client care insight:

• Shorten times to goal on your cases. Execute shrewd steering to the right line for people and furthermore use chatbots for those simpler, self-serve issues.

• Increase consumer loyalty by assisting clients with directing them to the best specialist. Then again, you can assist those specialists with being powerful by suggesting goals, articles or subjects relying upon the need of the client. What’s more, use ML to assist with surfacing significant client history to the client assistance agent.• Reduce cost by proactively messaging clients who seem as though they’re looking for things on your site.

• Perform main driver investigation. Attempt to mine information or investigate models to check whether you can — in view of models that can foresee something — dive into what is generally prescient and use it as an approach to work on an item or cycle.

So since we realize how to use ML in a client assistance setting, what does it truly take to construct a framework that uses ML?

As a matter of first importance, it takes shrewd individuals who follow cycles and use innovation to plan and fabricate savvy frameworks to give the best client experience conceivable. In light of my experience, the interaction assumes a key part and in this unique situation, I am discussing organizations focusing on computerized change by utilizing the most recent and arising innovations.

According to an innovation viewpoint, the excursion should begin with open source apparatuses and bundles with regards to planning your frameworks. The essential explanation behind using open source is a result of the wide scope of choices and that it helps minimize the expenses. Tensorflow, H2O.ai, and Microsoft Cognitive Toolkit are only a couple models.

Taking everything into account, connect business and innovation. Once, individuals, cycles, and stages are associated together, then, at that point driving ROI is simpler. A similar reality applies when endeavoring to further develop the client experience by utilizing AI.