Artificial Intelligence - A winning strategy for FinTech Industry

April 29, 2022

With the development of financial services, there have been constant efforts to develop more secure and easy transaction systems. 

Table of Content-

  • Fintech: Overview
  • Fintech Market Size 
  • How Fintech companies are using AI
  • Conclusion

Fintech : Overview

Fintech refers to any technology or innovation used in the financial sector to compete with traditional financial methods for providing financial services to customers.

Fintech has a long and illustrious history that extends back to the nineteenth century and even before. In 1860, a gadget known as the PANTELEGRAPH was created to help banks verify signatures. Historians consider 1866 to be the first year for the imprints of Fintech.

Fedwire's establishment of electronic financial transfers via telegraph and Morse code in 1918 was the initial baby step towards the digitalisation of money.

Fintech 1.0

The pioneer stage in the evolution of fintech is marked by Diner's Card. It was the first sincere venture to enable cashless payments in 1950. Heeding this, Amex brought up the Credit Card in 1958. With Quotron's debut of screen-based stock data in 1960, the financial industry took a giant leap forward. 

Fintech 2.0

The second stage of the development of fintech is supposed to have started with Barclay's launch of  the ATM in 1967. With the establishment of NASDAQ as the first electronic stock market in 1971, substantial fintech growth occurred. Fintech 2.0 marked the modernizing of the IPO process, the introduction of swift, development of electronic trades and online banking systems, online banking, the evolution of e-commerce, and the launch of PayPal. Fintech 2.0 finished up with the financial crisis of 2008.

Fintech 3.0

The advancement of stricter regulatory compulsions for traditional banks, cutting down the operational cost using technology, and the advent of new technologies like wallets, bitcoins, and P2P was all the development that made Fintech 3.0 very special and important in the steps towards a more sophisticated financial sector. 

Fintech 3.5

India and China were the only two countries where the financial sector glimpsed notable growth after the year 2014. These two countries moved away from the domination of the western world and acknowledged the magnitude of the improvement of digital marketing. The year 2017 marked the end of Fintech 3.5.

Fintech 4.0

Blockchain and AI are at the heart of Fintech 4.0. Whether it's for money transfer, processing and payments infrastructure, wealth management, or consumer lending, there are a slew of decentralized apps in the works.

AILabpage.com - Image credit

Fintech Market Size

The world has noticed extraordinary technological betterment and, as a result, widespread application of technology in one of the most essential industries - banking. 

According to a forecast published by BLinC Insights, the Indian financial services business was worth $500 billion in 2021, with the FinTech market accounting for $66 billion of that. The rising digitalization has aided the rapid expansion of the FinTech sector

The Indian fintech industry has total funding of over US$27.6 billion, according to Amitabh Kant (CEO, NITI Aayog), and is estimated to be worth over US$150 billion by 2025.

In India, there are over 2100 Fintech companies, with more than 67 per cent having been founded in the last five years. Fintech funding in India has risen at an exponential rate. There are currently 10 unicorns in the Indian FinTech business, including Razorpay, CRED, Pinelabs, PolicyBazaar, and 52 unicorns (soon to be unicorns), including Navi, Mswipe, and Lendingkart.

How Fintech companies are using AI?

AI use in fintech is driven by the need to forecast user behavior and better service clients. Artificial intelligence (AI) is utilized in FinTech for a wide range of applications, including lending decisions, customer service, fraud detection, credit risk assessment, insurance, wealth management, and more. 

Modern FinTech firms are employing artificial intelligence (AI) to boost efficiency, precision, and query resolution speed. The fintech market is predicted to increase as an outcome of this aspect. Because of the increased usage of AI interfaces and chatbots for efficient customer support, the AI category will give a lucrative potential for growth in the fintech market share.

Below is the list of 6 areas where AI is being used intensively by    the companies:

  1. Lending Accurate Decisions:  Insurance executives and future banking agents will ask the proper questions to robots rather than human experts as a result of data-driven management decisions at a reduced cost. Machines will then analyze the data and provide recommendations, which will aid leaders and subordinates in making better decisions.
  1. Customer Service: Text chats, audio systems, and Finance Chatbots are examples of customer-facing technologies that can provide human-like customer care or expert assistance at a cheaper cost. Automation and chatbots allow Fintech companies to save time and money: As your Finance Assistant, chatbots can help.
  1. Fraud Detection & Claim Management: Analytics software collects and analyses evidence necessary for a conviction. Artificial Intelligence technology then analyzes and tracks user behavior trends to detect fraud attempts and outliers. At many phases of the claim handling process, AI can be applied to improve claim management. Insurers can use it to automate handling procedures and manage vast amounts of data in a short amount of time. It can even expedite certain claims, reducing overall processing time and handling costs while improving client satisfaction. To assist in the detection of fraudulent claims, these algorithms hunt for trends in data.
  1. Credit Risk Assessment: The use of AI in credit risk management is still in its early stages, but the combination of an exponential increase in the amount of data accessible and improved machine learning algorithms to digest it has the potential to have a significant influence. Early warning signals are often utilized in credit risk management to identify organizations at higher risk of default before bankruptcy. Traditional early warning systems rely significantly on expert judgment and require a large number of experimentally defined indicators. AI is particularly good at detecting patterns in big amounts of data with a high velocity that can be utilized to generate credit default alerts. With enough computing capacity, AI algorithms may provide early warning signals based on signs from a variety of sources, as well as improve the indicator's accuracy.
  1. Insurance: Insurance management using AI technologies will streamline the underwriting process and make better recommendations for consumers by utilizing more unrefined data. Users can calculate their insurance needs with the help of online, automated agents. After a loss has happened, insurance frequently enters the scene. By integrating many relevant data sets, including external ones not available in the medical records, automatic underwriting can drastically speed up the process and often eliminate the need for costly tests. Rather than paying for expensive insurance treatments, it is preferable to recognize hazards and diseases early enough to prevent them. As a result, one can use the data that was previously used to assess risks to reduce the likelihood of losses occurring to the insured.
  1. Wealth Management:  Artificial intelligence-powered smart wallets track and learn from the users' actions and behaviors. These instruct consumers on how to limit and change their financial spending to save money. Artificial intelligence systems aid in the identification of a client's financial preferences and provide customized and curated recommendations. It informs the wealth manager about the client's risk tolerance and by analyzing data and presenting it in the context of relationships between customers, markets, products, and client profiles, it saves time for wealth managers. Furthermore, no suggestion or conclusion is instinctive while working with an algorithm. Such software is heavily regulated, and it is required to offer appropriate justification for any recommendations.

Concluding Words-

Because of the tremendous potential benefits, the Financial Industry will see major growth in automation, Artificial Intelligence being used more frequently. In finance, artificial intelligence, machine learning, and bots are no longer the stuff of science fiction. They can expand talents, reduce expenses, and improve customer experience.

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This is a decorative image for: Actions CEOs can take to get the value in Cloud Computing
September 28, 2022

Actions CEOs can take to get the value in Cloud Computing

It is not a new thing to say that a major transition is on the way. The transition in which businesses will rely heavily on cloud infrastructure rather than having their own physical IT structure. All of this is due to the cost savings and increased productivity that cloud technology brings to these businesses. Each technological advancement comes with a certain level of risk. Which must be handled carefully in order to ensure the long-term viability of the technology and the benefits it provides.

And CEOs are the primary motivators and decision-makers in any major shift or technological migration in the organization. In the twenty-first century, which is a data-driven century, it is up to the company's leader to decide what and how his/her organization will perform, overcome the risk and succeed in the coming days.

In this blog, we are going to address a few of the actions that CEOs can take to get value in cloud Computing.

  1. A Coordinated Effort

As the saying goes, the more you avoid the risk, the closer it gets. So, if CEOs and their management teams have yet to take an active part or give the necessary attention that their migration journey to the cloud requires, now is the best time to start top-team support for the cloud enablement required to expedite digital strategy, digitalization of the organization, 

The CEO's position is critical because no one else can mediate between the many stakeholders involved, including the CIO, CTO, CFO, chief human-resources officer (CHRO), chief information security officer (CISO), and business-unit leaders.

The move to cloud computing is a collective-action challenge, requiring a coordinated effort throughout an organization's leadership staff. In other words, it's a question of orchestration, and only CEOs can wield the baton. To accelerate the transition to the cloud, CEOs should ask their CIO and CTO what assistance they require to guide the business on the path.

     2. Enhancing business interactions 

To achieve the speed and agility that cloud platforms offer, regular engagement is required between IT managers and their counterparts in business units and functions, particularly those who control products and competence areas. CEOs must encourage company executives to choose qualified decision-makers to serve as product owners for each business capability.

  1. Be Agile

If your organization wants to benefit from the cloud, your IT department, if it isn't already, must become more agile. This entails more than simply transitioning development teams to agile product models. Agile IT also entails bringing agility to your IT infrastructure and operations by transitioning infrastructure and security teams from reactive, "ticket-driven" operations to proactive models in which scrum teams create application programme interfaces (APIs) that service businesses and developers can consume.

  1. Recruiting new employees 

CIOs and CTOs are currently in the lead due to their outstanding efforts in the aftermath of the epidemic. The CEOs must ensure that these executives maintain their momentum while they conduct the cloud transformation. 

Also, Cloud technology necessitates the hire of a highly skilled team of engineers, who are few in number but extremely expensive. As a result, it is envisaged that the CHRO's normal hiring procedures will need to be adjusted in order to attract the proper expertise. Company CEOs may facilitate this by appropriate involvement since this will be critical in deciding the success of the cloud transition.

  1. Model of Business Sustainability 

Funding is a critical component of shifting to the cloud. You will be creating various changes in your sector, from changing the way you now do business to utilizing new infrastructure. As a result, you'll have to spend on infrastructure, tools, and technologies. As CEO, you must develop a business strategy that ensures that every investment provides a satisfactory return on investment for your company. Then, evaluate your investments in order to optimise business development and value.

  1. Taking risks into consideration 

Risk is inherent in all aspects of corporate technology. Companies must be aware of the risks associated with cloud adoption in order to reduce security, resilience, and compliance problems. This includes, among other things, engaging in comprehensive talks about the appropriate procedures for matching risk appetite with technological environment decisions. Getting the business to take the correct risk tone will necessitate special attention from the CEO.

It's easy to allow concerns about security, resilience, and compliance to stall a cloud operation. Instead of allowing risks to derail progress, CEOs should insist on a realistic risk appetite that represents the company plan, while situating cloud computing risks within the context of current on-premises computing risks and demanding choices for risk mitigation in the cloud.

Conclusion

In conclusion, the benefits of cloud computing may be obtained through a high-level approach. A smooth collaboration between the CEO, CIO, and CTO may transform a digital transformation journey into a profitable avenue for the company.

CEOs must consider long-term cloud computing strategy and ensure that the organization is provided with the funding and resources for cloud adoption. The right communication is critical in cloud migration: employees should get these communications from C-suite executives in order to build confidence and guarantee adherence to governance requirements. Simply installing the cloud will not provide value for a company. Higher-level executives (particularly the CEO) must take the lead in the digital transformation path.

This is a decorative image for: Top 12 skills a CEO should demand in a data scientist to hire in 2022
September 21, 2022

Top 12 skills a CEO should demand in a data scientist to hire in 2022

Two decades ago, data scientists didn’t exist. Sure, some people cleaned, organized and analyzed information — but the data science professionals we admire today stand at the head of a relatively new (and vaunted) career path.

It is certainly one of the most popular careers because it is in great demand and highly paid. With data being the primary fuel of industry and organization, company executives must now determine how to drive their company in this rapidly changing environment. Not only is a growth blueprint essential, but so are individuals who can put the blueprint into action. When most senior executives or human resource professionals think of data-driven employment, a data scientist is the first position that comes to mind.

In this blog, we will discuss the top 12 skills a CEO should demand if hiring a data scientist in 2022. 

  1. Problem-Solving and Critical Thinking

Finding a needle in a haystack is the goal of data science. You'll need a candidate who has a sharp problem-solving mind to figure out what goes where and why, and how it all works together. Thinking critically implies making well-informed, suitable judgments based on evidence and facts. That means leaving your own ideas at the door and putting your faith - within reason - in the evidence. 

Being objective in the analysis is more difficult than it appears at first. One is not born with the ability to think critically. It's a talent that, like any other, can be learned and mastered with time. Always look for a candidate who is prepared to ask questions and change his/her opinion, even if it means starting over.

  1. Teamwork 

If you go through job listings on sites like Indeed or LinkedIn, you'll notice one phrase that appears repeatedly: must work well in a team. Contrary to popular belief, most scientific communities, including those in data science, do not rely on a single exceptional mind to drive forward development. A team's cohesiveness and collaboration power are typically more significant than any one member's brilliance or originality. Your potential candidate will not contribute to success if s/he does not play well with others or believes that s/he does not require assistance from your colleagues. If anything, candidates' poisonous attitudes may cause stress, decreased levels of accomplishment, and failure on the team.

Harvard researchers revealed in 2015 that even "moderate" amounts of toxic employee conduct might increase attrition, lower employee morale, and reduce team effectiveness. Eighty percent of employees polled said they wasted time worrying about coworker incivility. Seventy-eight per cent claimed toxicity had reduced their dedication to their work, and 66 per cent said their performance had suffered as a result. The fact is that being a team player is significantly more productive and fulfilling than being a solo act. Look for a candidate with good cooperation abilities, and both you and your team will profit!

  1. Communication 

Capable data scientists must be able to communicate the conclusions they get from data. If your candidate lacks the ability to convert technical jargon into plain English, no matter how significant the results are, your audience will not grasp them. Communication is one of the most important skills a data scientist can learn — and one that many pros struggle with. 

One 2017 poll that tried to uncover the most common impediments that data scientists encountered at work discovered that the majority of them were non-technical. Among the top seven barriers were "explaining data science to others," "lack of management/financial support," and "results not utilised by decision-makers."

You fail if you can't communicate - therefore look for a candidate who knows how to interpret! And can break down complicated topics into digestible explanations; rather than giving a dry report.

  1. Business Intelligence 

Sure, a candidate can’t start teaching abstruse mathematical theory whenever you want — but can they explain how that theory can be applied to advance business? True, data scientists must have a strong grasp of their field as well as a solid foundation of technical abilities. However, if a candidate is required to use those abilities to advance a corporate purpose, they must also have some level of business acumen. Taking a few business classes will not only help them bridge the gap between their data scientist peers and business-minded bosses, but it will also help them advance the company's growth and their career as well. It may also assist them in better applying their technical talents to create useful strategic insights for your firm.

  1. Statistics and mathematics 

When it comes to the role of arithmetic in machine learning, perspectives are mixed. There is no disputing that college-level comprehension is necessary. Linear algebra and calculus should not sound like other languages. However, if you're looking for a candidate for an internship or a junior position, then they don't need to be a math guru. But if you are looking for a candidate to work as a researcher, then the candidate must have more than just a strong math background. After all, research propels the business ahead, and you won't be able to accomplish anything until you have a candidate with a thorough grasp of how things function.

The fact is that just because data science libraries enable data scientists to perform complex arithmetic without breaking a sweat doesn't mean they shouldn't be aware of what's going on behind the surface. Get a candidate with the fundamentals right.

  1. AI and Machine Learning 

Machine learning is an essential ability for any data scientist. It is used to create prediction models ranging from simple linear regression to cutting-edge picture synthesis using generative adversarial networks. When it comes to machine learning, there is a lot to look for in a potential candidate. Regression, decision trees, SVM, Naive Bayes, clustering, and other classic machine learning techniques (supervised and unsupervised) are available. Then there are neural networks, which include feed-forward, convolutional, recurrent, LSTM, GRU, and GAN. There's also reinforcement learning, but you get the idea - machine learning is a vast subject. 

  1. Skills in cloud and MLOps

To remain relevant to the industry's current demands, more than three out of five (61.7%) companies say they need data scientists with updated knowledge in cloud technologies, followed by MLOps (56.1%) and transformers (55%). Three out of every four professionals with ten or more years of experience are learning MLOps to expand their skill sets. Cloud technologies (71.7%) are being learned as a fundamental new talent by mid-career professionals with 3-6 years of experience, followed by MLOps (62.3%), transformers (60.4%), and others.

Professionals in retail, CPG, and e-commerce are more likely (73.7%) to learn cloud technology as a new skill. As much as 70% of BFSI personnel upskill in MLOps. Another 70% and 60% of pharma and health workers are interested in acquiring transformers and computer vision as fundamental skills.

So make sure you don't miss out on such a talent who can bring cloud and MLOps skills into your company. 

  1. Storytelling and Data Visualization 

Data visualisation is enjoyable. Of course, it depends on who you ask, but many people consider it the most gratifying aspect of data science and machine learning. Look for a candidate who is a visualisation specialist and understands how to show data based on business requirements, and also how to integrate visualisations so that they tell a story. It might be as easy as integrating a few plots in a PDF report or as sophisticated as creating an interactive dashboard suited to the client's requirements.

The data visualisation tools utilised are determined by the language. Plotly, which works with R, Python, and JavaScript, may be the best option if you need a candidate for searching for a cross-platform interactive solution. Consider Tableau and PowerBI when you need a candidate for viewing data using a BI tool. 

Figure: Use of Data Visualization tools. 

  1. Programming 

Without programming, there is no data science. How else would you give the computer instructions? All data scientists must be familiar with writing code, most likely in Python, R, or SQL these days. The breadth of what a candidate will perform with programming languages differs from that of traditional programming professions in that they’ll lean toward specific libraries for data analysis, visualisation, and machine learning. 

Still, thinking like a coder entails more than just understanding how to solve issues. If there is one thing that data science sees a lot of, it is issues that need to be solved. But nothing is worse than understanding how to fix an issue but failing to transform it into long-lasting, production-ready code.

Out of the host of programming languages, 90% CEOs hire data science specialists who are specialists in Python as their preference for statistical modelling. Beyond that, the use of SQL (68.4%) is highest in retail, CPG, and ecommerce, followed by IT at 62.9%. R is the most widely used programming language if you operate in the pharma and healthcare business, with three in five (60%) data scientists reporting using it for statistical modelling.

  1. Mining Social Media 

The process of extracting data from social media sites such as Facebook, Twitter, and Instagram is referred to as social media mining. Skilled data scientists may utilise this data to uncover relevant trends and extract insights that a company can then use to gain a better knowledge of its target audience's preferences and social media actions. You need data scientists well versed with this type of study as it is essential for building a high-level social media marketing plan for businesses. Given the importance of social media in day-to-day business and its long-term viability, hiring data scientists with social media data mining abilities is an excellent strategy for company growth.

  1. Data manipulation 

After collecting data from various sources, a data scientist will almost surely come across some shoddy data that has to be cleaned up. You need to hire a candidate that knows what Data wrangling is. How to use it for the rectification of data faults such as missing information, string formatting, and date formatting. 

  1. Deployment of a Model 

What is the use of a ship if it cannot float? Non-technical users should not be expected to connect to specialised virtual machines or Jupyter notebooks only to check how your model operates. As a result, the ability to deploy a model is frequently required for data scientist employment.

The easiest solution is to establish an API around your model and deploy it as any other application — hosted on a virtual machine operating in the cloud. Things get harder if you wish to deploy models to mobile, as mobile devices are inferior when it comes to hardware. 

If speed is critical, sending an API call and depending on an Internet connection isn't the best option. Consider distributing the model directly to the mobile app. Machine learning developers may not know how to design mobile apps, but they may examine lighter network topologies that will have reduced inference time on lower-end hardware.

Consider hiring a candidate who is well versed with all the things discussed above related to deploying a model. 

Conclusion

And there you have it: the top twelve talents skills a CEO must look for while hiring a data scientist. Keep in mind that skill levels or talents themselves may differ from one firm to the next. Some data science jobs are more focused on databases and programming, while others are more focused on arithmetic. Nonetheless, we believe that these 12 data science skills are essential for your potential candidate in 2022.

This is a decorative image for: Towards Complete Icon Labelling in Mobile Applications
September 21, 2022

Towards Complete Icon Labeling in Mobile Applications

Why is Icon Labeling Important?

Icon labeling projects aim to create a machine learning algorithm that can automatically label icons in mobile applications. The algorithm is generally trained on a dataset of labeled images and learns to recognize the objects in the pictures. Labeling icons is a tedious task and often requires human intervention.

Thus, automating this process by training an algorithm on a labeled image dataset can pave the way for complete icon labeling. This article will walk you through labeling icons using machine learning. Icons may seem like a small part of your app, but they're critical for branding and user experience. Icons need to be labeled by hand, which is time-consuming and tedious.

It isn't easy to keep up with the volume of new icons on mobile phones, and keeping the icons organized takes a lot of effort. The wrong icon can ruin your app's design and make it difficult for users to use. With any icon labeling project, labeling icons is easy. Your database will be automatically and consistently labeled by Artificial Intelligence that recognizes objects in images.

How to Label Icons Effectively?

Prepare your data set. It should include the icon's name, a short description, and an image of the icon. You can use any file type uploaded to any storage or drive.

Next, you will need to create a project in the platform and enable billing if it has not been done already. Then you can create a new dataset by specifying a dataset ID and name.

The use of labeling icons in UI design has been around for many years. The most popular use case is to offer users an indication of what they can do on a particular screen. You can do so by adding labels to the icons.

Icons often indicate the user's action to complete a task (e.g., save, delete, etc.). However, this could be problematic for people with disabilities or who cannot understand or read English fluently due to language and communication barriers.

Labeling icons is complex, especially when the icon is not well-known. We propose a novel method for labeling icons with conversational agents and chatbots. Machine Learning techniques can help generate a set of labeled examples for a conversational agent or chatbot training.

Tips for using icons in your app

Labels are the most critical component of an icon, as they communicate the meaning to users. Designers should keep their icons simple and schematic and include a visible text label to make them good touch targets.

Icon designers also need to be careful when designing icons. Designers should keep their icons simple and schematic, include a visible text label and make them good touch targets. Labels are the most crucial component of an icon as they communicate meaning to users.

Icons should be simple and schematic with a clear visible text label that communicates what the icon means to users. Icons are also suitable for touching targets for screen readers, so designers must consider this when designing them.

Icon labels are an essential feature that can make or break an icon. Designers are often designing icons with less-than-perfect or downright nasty labels. Terrible labels can lead to misinterpretation and confusion, leading to lost business or a tarnished reputation. Labels are not just crucial for designers; they're critical to users.

The label conveys the meaning of a symbol, so it should be simple, visible, and easy for interaction purposes. If designers ignore these principles, icons will become meaningless, unhelpful, and challenging to navigate. Designers must create good touch targets that are easily recognizable. After all, it's about bringing users the best.

Conclusion

Iconography is the basis of every UI design. Designers need to understand how it shapes an interface’s usability. Every icon in an interface serves a purpose. When implemented carefully and in the correct manner, icons can help users navigate through the workflow. It's good to be a part of this cutting-edge iconography which can help you further push the boundaries of Deep Learning and expand your understanding of recognizing icon types.

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