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Sunday, 14 February 2021

What is likely to happen? The journey of Prescriptive Analytics

 This is the final frontier in data analytics-- the ability to respond in real time to event data in a way that allows you to back the instincts of the decision maker with data. If your data is telling you what your instincts are saying, the chances of making an informed decision to influence outcomes increases.

However, the right questions have to asked of the data otherwise the outcome will not be what was desired.



Some examples in banks where this type of data analysis is prevalent:

Customer insights based on NLP based sentiment analysis which can be fed into the mobile app

Trading intelligence on what stocks to buy

Better credit scoring by using non traditional sources like social likes .


What can happen? The story of Predictive Analytics

Predictive Analytics is the process of using a combination of data analysis, statistics and machine learning to make predictions on possible outcomes.

Examples of these can be:

-predicting when a turbine in a power plant fail operating --this can help schedule maintenance activities  

-predicting customer behaviour based on time of our marketing promotions

-predicting capital required to meet regulatory requirements based on the volume of loans a bank has given in a particular period of time.







We use well established statistical tools in machine learning also known as supervised machine learning where we are trying to arrive at a number to predict the outcome. The fact that you are able to use a data set to run an ML algorithm means you are well advanced in the journey of analytics. It assumes you have the sources of data in the format you need and they are able to work inside a model developed for the purpose of prediction of outcome.

Thursday, 11 February 2021

Why did it happen? The Story of Diagnostic Analytics

 Diagnostic Analytics tries to answer the why something happened in the past  from our data sets- for example why did sales rise this month though we didn't have any marketing campaigns, why did patients in a sample have the same symptoms,why did our bank's customers choose the 5 year fixed mortgage product.



Frequently this side of data analytics looks at both internal and external data sets to draw inferences . The process of drawing out the inferences can lead to a significant benefit-- the creation of a data mindset in the company that will have force multiplier effects on the company's future performance . The process itself leads to exposure to tools for data drill down, statistical methods and confidence in future decision making --all leading to a virtuous cycle of creating a data mindset.


Monday, 8 February 2021

What happened? The story of Descriptive Analytics

 The oldest know data analysis consists of two things-aggregating or summarizing data and  data mining.

The majority of a company's investment in data analytics is focused on aggregating data-- what was the sales, by month, by region for example.Comparing to previous time periods gives an understanding of where the company is going for instance the sales have increase 20% compared to previous quarter. It is an important tool for analysis of the performance of a company. Different types of data are then mixed and matched to provide further insights such as :

ROIC--Return on Invested Capital takes  three datasets--net income,dividends and capital .This allows us to compare performance of two or more companies.

On a similar vein, company annual reports are descriptive analytics in action . They tell the story of what happened in the past. These reports are mostly derived from spreadsheets and ancillary sources such as ERP tables .



Data mining techniques allow you run the group aggregated data and look for logical conclusions to understand customer behaviour. A grocery chain may use data from its loyalty card program group to analyze what are some of the fast moving items in the produce section.

Banks are one of the best examples of the descriptive analytics mindset. They are so focussed on the historical aspects which is important since they are custodians of our money. The problem is both legacy systems and the lack of new data mindset is making it very hard for them adopt the newer types of data analytics 




Thursday, 4 February 2021

Why are banks interested in your data?

 Why are banks interested in customer data? The answer is one word--Steve Jobs.

On 29 June 2007 the iPhone was launched. Fast forward 15 years  and the reverberations of iPhone are still being felt. The pandemic has only accelerated these changes.



Steve said:

 "A lot of times people don't know what they want until you show it to them"

What did Steve do that was so revolutionary?




He showed that a customer centric unified value proposition can be created by bridging gaps along the value chain, providing the customer with new experiences and whetting their appetite for more.

This results in an emotional connect for the customer. Apple had a $100 billion profit quarter. I rest my case.

The takeaway for banks is that a customer centric value proposition has to be created. This needs the capacity to collect a large amount of data. The data when translated to actionable business insight helps predict customer journey thereby creating the emotional connect



This is the reason why inspite of lack of a clear ROI , banks have been pouring billions in collecting and understanding  customer data. 

Now they have to solve for a seamless value chain-- a problem fraught with hubris, legacy systems, lack of a data mindset. New customer experiences and whetting the appetite will follow once these hurdles are overcome.






Wednesday, 20 January 2021

Where is the action in retail payments?

 In the beginning there was gold  followed by cash. Then came the check. And then came technology where we are now.



The best analogy to understand  the action in retail payments is that of a kitchen table where the transaction occurs. The table has 4 legs and the table top. 

The four legs together with the table top  in our kitchen table example are the basic requirements of creating a transaction.



Customer:

The  first leg is a card holder who wants to purchase goods or services for which the payment card is used. Many flavors of cards exist- credit card, debit card, prepaid card and virtual cards. With the rise of smart phones a new breed of payment vehicles called digital wallets has emerged. Here you load funds from your bank account. Open Banking allows you to choose from the smart phone which bank you want to use to load funds.

The action is in offering the customer  multiple choices in making payments--cards, wallets, QR codes, Buy now pay later(BNPL), Request to pay(RTP), EMT or email money transfer, account to account to split a restaurant bill to name a few. 

Merchant:

This second leg is the entity that sells you the goods or services. This can take place in a physical store or online in a website or app. The payment is accepted in a Point of sale terminal or  in a secure area of their website called Payment Gateway where you enter the card details. In facilitating a payment, they accept any of the payment vehicles  the customer chooses to pay. Once received, they send the transaction over to the third leg called processor.

The action is in enabling Merchants accept the various payment vehicles. This is driven by the need to offer convenience and personalization to the customer which ultimately drives revenue.

Processor:

The third leg goes by various names --acquirer, payment processor, PSP, merchant aggregator. Sometimes a bank can offer the processing. With the rise in transaction types the action for the processors is making sure the transaction goes through and also to route the transaction at the least cost  for themselves and the Merchant. 

Issuer:

The fourth and final leg of our kitchen table analogy  is a bank or fintech who has issued the card or payment vehicle to the customer. These entities are responsible for funding the transaction by taking money from the customer bank account  and sending it on to the Merchant's bank account. 

The action here is that the Issuer has to provide these new payment vehicles to the customer . The success lies in how well they market the vehicle leading to more acceptance in the market.


Networks:

Finally the table top or the network that connects all the parties . They are the trusted brands we know so well like Visa, Mastercard, Amex. New payment networks based on current technologies are disrupting the legacy Networks business models. This is where the action lies.

 These new  networks are domestic networks in nature ,usually driven by the government and are provided to increase the availability of new payment vehicles . The goal of these governments is to enable transition from cash and financial inclusion. Absent legacy costs, these networks offer speed , convenience and lower costs to all the legs of the kitchen table top in the payment ecosystem. Examples of these networks are Interac in Canada, UPI in India, M-pesa in Kenya, SEPA in EU.

The legacy networks are not resting . They are also building these new networks in addition to their legacy infrastructure.

Some of the technology building blocks that enable this action in the retail payments area include ISO20022 payment messaging, tokenization, digital identity. They all take advantage of rich data now available about customer behavior to reduce operational risk elements like failing transactions in straight through processing, fraud mitigation, cloud infrastructure to cut costs and digital trends to attract new customers.

All in all a great time to be in this space.












Saturday, 16 January 2021

Embedded Finance and Payments

 We have seen a lot of exciting developments in the consumer space introducing payments as a way to enhance revenue and customer interaction. Starting with major tech players like Apple and Google offering wallets for paying merchants, Square added debit cards to its Cash App P2P platform, Uber used the Visa  rails to provide instant payments to its drivers to Shopify adding Stripe's payment processing capability for its merchants.

These initiatives have led to revenue gains for all parties but more importantly taken away customers which would have belonged to banks. JP Morgan's CEO was moved to announce that some of these fintechs are a threat to their business. Couple with the valuations these tech companies are getting in the tens of billion in many cases , you can understand the impact embedded finance is having on the traditional banking industry.

How are these results possible? Its possible by embedding the payment APIs into the company's business process such that the consumer can take advantage of the rich UXs the fintech offers and it gives benefits in terms of data about the customer behavior. 

With ISO20022 payment message standard gain traction, the benefit of embedding payment flows and interactions with the data generated will be an opportunity for serving the B2B sector as well with products such as virtual card for vendor payments.