What is FinTech?

Fintech [Financial Technology] describes the innovative technologies that are making the financial services industry more efficient, personal, and secure. Powered by innovations within mobile, big data, machine learning, and blockchain technology, companies within Fintech are working to disintermediate or bypass incumbent financial players and challenge traditional institutions with new solutions that are changing applications, processes, products, and business models.

Source: Ark Invest

 

What is Financial Analytics?

Financial analytics is a term used for the concept that give different views on business’ financial data. It gives in-depth knowledge and take strategic actions against them to improve your business’ overall performance. Financial analytics is a subset of BI & EPM and has an impact on every aspect of your business. It plays a crucial role in calculating your business’ profit. It helps you answer every business question related to your business while letting your forecast the future of your business.

 

ABCDs of FinTech

AI

The term AI was coined at the famous Dartmouth summer research workshop. AI can be classified into a narrow or weak AI which relates to algorithm performing specific tasks and general or strong AI signifying broader human intelligence and decision-making. There are also different strands within AI including the Natural Language Processing (NLP) which relates to the rule based analysis of written language & Machine Learning which is associated with learning from experience and performing predictive analysis based on data training. 2017 was named the year of AI by both Forbes & Fortune due to confluence of a few events – First, large expensive supercomputers used for AI algorithmic processing were replaced with cheaper Graphical Processing Units (GPUs) with increased computational power originally produced for video games & Secondly, data storage costs continued to fall thus the huge amounts of data being collected through online activity & connected devices could now be gathered, stored and used much easily to train the machines. And finally, Major Cloud companies like Amazon’s AWS, Microsoft’s Azure, Google Cloud & Alibaba’s Aliyun incorporated AI into their services which included Open source Machine Learning frameworks which the client can experiment and incorporate easily into their operations. AI is rapidly changing the Fintech interfaces varying from facial & voice recognition for identity management to chatbots, and can then provide personal recommendations based on clients’ preferences and algorithmic matching of needs. AI has also helped some alternative finance companies to venture into new business models of intelligent analysis of customer data rather than just providing fund flow. For example, some Chinese Fintech startups which started off as P2P lending platforms have moved to providing credit analysis & scoring to institutional investors and lenders that are serving in the lending marketplace.

Blockchain

Distributed Ledger Technology (DLT) forms the basis around which the concept of Blockchain evolved – in simpler terms it is the reliable documentation of creditor & debtor in a standardized manner. The conspicuous Sataoshi Nakamoto outlined the establishment of a digital currency (or cryptocurrency) that seeks to solve the double spending problem intrinsic to the traditional fiat currency. Nakamoto envisioned the idea of setting up a digital ledger via a peer-to-peer network which does not require the supervision of a central authority. The transactions are recorded by way of blocks – each block is validated by solving a cryptographic mathematical puzzle (or hashes), at the end of which the new data block is added to the existing chain with a new cryptographic hash & time stamp transparent to all users. Data in this block or the previous ones cannot be altered unless a consensus or agreement is reached. Blockchain is thus the underlying technology behind Cryptocurrencies.


Cloud

In the past financial institutions have relied on on-site IT systems which relied on enterprise level software developed and/or licensed at a high cost. This was a time-consuming & expensive process which required a lot skilled manpower and financial undertaking. With the advent of Cloud Computing this software now resides on the servers of the data centers of the companies who are specialized in providing these kind of services. On top of the customized built-in solutions these companies also provide value added services like Cyber security protection which has become a key component of any solution involving processing of big data. This of course meant that Fintech startups & Alternative Finance providers can now pool & direct their resources towards improving the client experience rather spending it on expensive IT infrastructure. They can also scale their server usage as dictated by their rates of growth. Cloud computing has encouraged new business models like SaaS (Software as a service) to emerge which allowed the these financial institutions to bypass the traditional vendor models involving development, sales, service, marketing & licensing. This cycle was repeated each time software was upgraded to a newer version. Software that resides in the cloud can be marketed at a lower cost since it is mostly based on a subscription model. It can also be upgraded on an ongoing basis which benefits the client with lower fees, convenience & peace of mind.

Data

For a long time the banks have relied upon a great deal of information generated from and about their customers – things like their identity, net worth, transactions & location etc. All of this information was collected via filling out a lot of paper forms by the clients and the bank employees. Obviously this huge pile of paperwork was neither easily searchable nor readily available for analysis. The digitization of all this data meant easy storage, transmission, search, analysis & processing of all this information. This provided the online market places to function cost effectively, efficiently while providing a great deal of convenience to the customers. Customer’s online behavior such as logging in to their virtual banking or e-brokerages, transactions performed and other online activities like web browsing, e-commerce & social media use is tracked for analysis. Increasingly, offline behavior is also being tracked via IoT devices like smart watches (Apple watch), smart home devices like Amazon echo and smart cars. No wonder the data has become the ‘New Oil’ being sought after equally by the gatherers & users. To put things into perspective Walmart, the world’s biggest retailer, processes 2.5 petabytes of data every hour – that is 16 zeros!

Source: Data Driven Investor

 

Six Key Financial Analytics

 

Predictive sales analytics

Sales revenue is the lifeblood of any business so knowing how much you can expect to receive has important tactical and strategic implications. Predictive sales analytics involves figuring out how successful your sales forecast is and improving your sales predictions in the future. There are many ways to predict sales, such as looking for trends in past data or using predictive techniques like correlation analysis.

Predictive sales analytics is an extremely useful tool for planning and peace of mind, helping you manage the peaks and troughs of your business. For example, many businesses experience more and less sales at certain times of the year. If you know that year on year you make fewer sales in July then you can encourage staff to take holiday then and stay calm when sales drop in that period.

Customer Profitabilty Analytics

It’s important to differentiate between the customers that make you money and the customers that lose you money. Customer profitability usually falls within the 80/20 rule, whereby 20% of your customers account for 80% of your profit, and 20% of your customers account for 80% of your customer-related costs. Knowing which is which is important.

By understanding the profitability of certain groups of customers you can also analyse each group and extract useful insights. For example, you may discover that your very best customers made their first purchase from a particular advertisement in a particular magazine. That knowledge can help direct your future marketing efforts

Product profitability analytics

In order to stay competitive, businesses need to know where money is being made and lost. Product profitability analytics is a way of discovering profitability by individual product, rather than looking at the business as a whole. To do this you need to assess each product and its costs individually. Admittedly, this can be tricky because your products may well share production processes or cost bases. Therefore, you need to find a reliable and fair way to apportion costs to your various products.

Product profitability analytics helps businesses uncover profitability insights across the product range so better decisions are made and profit is protected and grown over time. For example, if you discover that one product makes more profit than all the others then you may want to promote that product more heavily.

Cash flow analytics


The day-to-day running of a business requires a certain amount of cash to keep the lights on, wages paid, etc. Knowing how money is moving in and out of your business is essential for gauging the health of your business. Cash flow analytics involves using retrospective or real-time indicators such as the Cash Conversion Cycle and Working Capital Ratio. You can also use tools like regression analysis to predict future cash flow.

On top of helping you manage cash flow and making sure you have enough cash to keep the cogs turning, cash flow analytics can also support a variety of corporate functions. For example, analytic software can help accounts receivable personnel to increase cash flow by prioritising which customers are contacted by collection staff and when.

Value driver analytics

Most businesses have a sense of where they are heading and what they are trying to achieve. Often these goals are formalized on a strategy map that identifies the value drivers in the business. These value drivers are the key levers that the business needs to pull in order to meet its strategic objectives. Value driver analytics is the assessment of these levers to ensure they actually deliver the expected outcome.

Value drivers are often based on assumptions which need to be tested to check they are correct. For example, you may use price as one of your value drivers and assume that price influences sales and revenue, but you need to test that hypothesis so you can establish if you are right or not.

Shareholder value analytics

The results and interpretation of the results by investors, analysts and the media will determine how successful your business is on the stock market. Shareholder value analytics is a calculation of the value of a company made by looking at the returns the business provides to its shareholders. It effectively measures the financial consequences of strategy and assesses how much value the business’s strategy is actually delivering to the shareholders.

Shareholder value analytics should be used frequently alongside profit and revenue analytics. To measure shareholder value analytics, you can use a metric called Economic Value Added (EVA). This calculates the profit of a business when the cost of equity finance has been removed.

Source: Forbes

 

Learning Materials:

Websites:


Podcasts:

Newsletters:

Courses:

  • Introduction to FinTech by the University of Hong Kong (Here)

  • FinTech Ethics and Risk by the University of Hong Kong (Here)

  • Blockchain and FinTech: Basics, Applications, and Limitations by the University of Hong Kong (Here)

  • Python for Finance: Investment Fundamentals & Data Analytics by 365 Careers (Here)

  • Classification-Based Machine Learning for Finance by Anthony NG (Here)

Videos:

  • How FinTech Can Positively Impact the World (Here)

  • How FinTech is Shaping the Future of Banking (Here)

  • Bank 4.0 and the Future of Financial Services (Here)

  • Europe’s FinTech Revolution (Here)

Books:

  • Bank 4.0: Banking Everywhere, Never at a Bank by Brett King (Here)

  • The FINTECH Book: The Financial Technology Handbook for Investors, Entrepreneurs and Visionaries by Suzanne Chichti (Here)

  • Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World by Alex and Don Tapscott (Here)

  • Blogs:

  • FinRegRag (Here)

  • Lend Academy Blog (Here)

  • Beyond Sandbox (Here)

  • The FinReg Blog (Here)

  • Wharton FinTech Blog (Here)

 

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