Getting change management right

 With the rapid pace of change and speed of digital transformation in the post Covid world, banks need to ensure that change management processes are robust and address all aspects need to bring about the change.

Consider the following from a McKinsey survey:

• Only 14% said they had realized significant performance improvements with these efforts.

• Only 3% said they had successfully sustained any changes

No wonder executives are sick of death by a 1000 POCs

Use this graphic to make sure your change management procedures check all the boxes.

A simple tool like this will assist and go a long way in achieving sustainable change

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Can Banks connect emotionally?

 

Where and how can banks connect emotionally with customers and why should they? The why is easy--connected customers are the most profitable customers who need the complete portfolio of services a bank can provide.

With the shift to digital , the pandemic and the rise of challenger banks, its table stakes to have a digital presence offering checking accounts to receive money and make disbursements. The challenge is how do you differentiate such that the customer chooses your bank. This is a function of how the marketing organization works and their ability to create a compelling environment for  customer to enter your bank digitally. Its a whole another topic.

But once the customer visits the branch for a meeting , this is the big opportunity to create the emotional connect. Customers visit the branch for three reasons:

• Solve a problem they are not able to do online.
• Looking for advise on best management for their money
• Education around digital tools available to them on a self service basis.

This is where the bank can make the emotional connections by providing well trained and knowledgeable staff to assist the customer. The interactions and outcomes of these meetings is where the emotional bond is strengthened. 

A culture of sharing knowledge and genuine desire to help will result in the emotional connection. The bank management has the job to make sure the tools are available and they are able to measure the outcomes and repurpose as needed.

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Barriers to Data Mindset

 It is very hard for a bank to develop a data mindset.  Traditionally, regulated banks have been trained to deliver accuracy as they are managing money. The focus is on getting it right without errors and making sure all compliance requirements are met. This culture does not reward any creative inputs. Legacy systems on which banks have spent decades managing are also inflexible. In addition the process of completing a transaction has to follow deeply structured processes.

This environment does not allow empowerment of any sort at the customer level. The demographics of the customers using the banking services have changed. They are younger, often more affluent and tech savvy . They are used to Amazon type services and Apple type user experiences. If a bank cant provide what they want, they will look to fintechs who can. Post pandemic this customer churn is hitting 15% .

So what can banks do? Its well established that a customer centric service paying attention to unique situation a customer is in their financial journey can make them happy campers. This level of service can be obtained by gathering and harvesting insights from the data the bank has about the customer. Banks have not found any meaningful ROI with their investments in data technologies. They still operate in silos so much so that a late 2017 study found they are valued at 54% below book value.

The answer lies in a top driven change management approach.

First, get the KPIs of the top level executives to include metrics on data initiatives, these should be tied to their bonuses. At the second tier of executives empower them resources on obtaining education on how data driven organizations work. At the third level get together small cross functional teams with clear risk and reward for targets identified to use data and improve operational processes, reduce time spent by customer on interaction with various channels to name a few. At the front line level make available the complete profile of the customer and recommendations on talking points when interacting with customer. Reward the frontline for usage and success stories making them stakeholders in the data mindset culture.

These are not impossible actions. Consider what a 5% customer retention means, a 15% uptick in new account openings, a 10% increase in loan book means to the bottom line of the bank and see the trade off between status quo and investing for a data mind culture in the bank

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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 .

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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.

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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.

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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 

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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.

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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.

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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.

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The business of banking and payments

 What does a bank do? From the perspective of a consumer of business here are the core functions:

-Offers consumers banking services around their day to day needs of payroll, paying bills, cash requirements, mortgages, deposits and managing wealth.

-Offers businesses a way to receive money from customers and pay their suppliers, loans to fund their business, handle domestic and international  payments 

Between themselves, they offer loan syndication, securitization, investments , stocks and bonds.

A common theme is the use of information technology to support their transactions and service their constituents with information about their money.

Since thousands of years this has been the function of banks. A trusted repository of your money and a facilitators of transactions for business benefit from financing wars to paying for your coffee. 

What has changed in the last two decades? The rise of internet as a set of technologies to enable banking. The unintended consequence is that Internet has enabled banks to take advantage of scale and hide bad behavior such as creating complex derivate trades without responsibility  for the underlying asset. 

But what about payments? The Internet has been of net benefit to the payments segment by enabling tracking of the legal beneficiaries and cost transparency for the end user of banking services. Trends like modernization of payment rails, APIs, Open Banking, ISO20022 messaging are facilitating deep integration across the payments value chain. This is a clean , fee based business with few places to hide for bad actors.

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Barriers to Open Banking

 As the banking industry is wrestles with  the twin challenges of  new customer acquisition and operational costs they are aligning some of their spend on digital transformation to Open Banking driven by the requirement of giving TPP or third party providers access to customer payments data.

This has created some additional barriers around competition and innovation. Lets consider these in more detail:

Competition:

-All jurisdictions with PSD2 have rules around which accounts should be accessible by TPPs. Many banks have even published APIs of their own, however there lack of standardization of these APIs. If banks build "premium" APIs the TPPs will be charged for this access.

-In market driven jurisdictions like USA and Canada they are talking of consumer rights but legislation is absent . In this case bank have partnered with TPPs like Plaid under a screen scraping contract to display account information . But these carry contractual risks as the recent litigation by TD bank has shown.

Innovation:

-A bank may hope to provide access to data residing in legacy mainframe systems, but will face manual work arounds when encountering the new authentication and authorization requirements for services offered by third parties.

-When a TPP wants access to data in absence of detailed rules around ownership of customer data banks may comply by providing the bare bones payment information. In such cases the 360 degree view of the customer is unavailable for TPPs to offer personalized service.

The solution is to think of Open Banking as market infrastructure and enable each other. The regulators also need to play an enabling role to ensure fair rules and partnership. This will take time to evolve.

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Why should banks care about ISO20022?

 Consider some of this data:

-Total transition costs for migration to ISO 20022 payment message standard cost the SEPA $9 billion as per a 2017 study.

-Payments Canada expects a migration from checks will save the Canadian economy $4.5 billion.

This is good for the economy and society as a whole but I am a bank running for profit so why should I care? I have a system that works fine, the inefficiencies and delays in my current system is to my benefit as I can enjoy the float and charge more plus its been working fine for decades.

Well too bad, bank. Customers and regulators are insisting on this change and if you don't change you risk rising operational costs. falling behind the curve leading to loss of customers and regulatory burdens.

Lets peel this onion, shall we?

Rising operational costs: Drop in STP rates means less and less of your transactions will be completed without manual intervention. As it is, only about 60-80 % of your transactions go through STP and this is already  impacting the cost vectors of your payment business. ISO20022 rich data messaging allows you to do better reconciliation leading to improved STP rates.

Regulatory burdens: Sanctions screening, AML, KYC, purpose of making the transaction, legal entity, ultimate beneficiary  all these are checks you are doing today. With tools like LEI in ISO20022 this information travels with the message reducing your regulatory obligations.

Loss of customer: If I am a customer expecting a wire transfer and its going to take a week to get the money in my bank account and cost me $40 for this  , I will certainly look for options to get the funds faster and for lesser cost. No brainer here.

So the cost of not moving to ISO20022 is not the factor .Rather, it is the when and what deadlines I am facing that drive this decision. See this chart and note that many market players in North America are yet to go live but be assured all regulators are working in this directions.

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What is a Data Fabric?

 We are all aware that banks today are dealing with multiple data sources. Each of these data sources or end points  have to be brought into a central warehouse to run analytics on.  All this data movement requires integration capabilities and further the need to manage for security , the information about the data or metadata.

Data fabric is a middle layer that sits on top these end points and the connections architecture . You can think of this as a logical or virtual layer. You then build services to connect to the data fabric.  It has the contextual capability which describes the lineage of the data and the business logic.

This infrastructure allows the consumer of this data a convenient data set for analysis regardless if the user is from a bank or the customer. This data set is always current and fast  because we are not copying the stored data and its rich because it comes from any number of data sources.

The benefit is for the bank that you don't have to make copies of the data , the fabric abstracts the data into a dataset  for analysis and for the end customer they can interact with their own data . Due to zero replication, it is easier to administer and manage the data thus providing them control over the data they shared with the bank.

The overall benefit for the bank is this type of infrastructure allows them to think of new ways to create products and provide insights to their customers. 

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How will payments be regulated?

 With the success of Chinese and UK based fintech and the changes in the USA allowing the likes of Amazon to offer lending to small business, fintechs are a well established member of the market infrastructure. As the volumes they handle rises the questions is what will regulators do?

On the one hand they want to encourage competition, enable the underserved and have worked to ensure data collections and sharing is regulated. Now that these goals have been met, the challenge is what to do with the new monopolies being created in fintech.

Witness the ANT group IPO shut down. Regulators will now want to ensure that no body is too big to regulate. The sheer scale of data about customer behaviour an Alibaba or Amazon can gather gives them an unbeatable advantage versus banks. These companies will also lock out new fintech competition by buying  them out or restricting access to data, the same problem that banks have.

One way is to ensure as in US that the parent company has liquid assets corralled off to cover the activities of their payments and microlending activities. The capital needed will satisfy regulators but want about innovation?

Would blockchain DLTs be the answer? By providing access to payment processors via DLT  they allow more entrants to what is essentially an old boys club and protect innovation perhaps.

It will be interesting to note the developments in regulations.

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What is a Data Repository

 In order to provide true value to the customer, banks need to understand them first. To understand customer behaviour with the goal of offering products/services relevant to them, the bank collects all details of the customer transactions. Then they study the data to see if any deeper understanding can be gleaned.

To facilitate the process of getting insights banks use a collection of data bases called Data Repository   (aka data lake, data mart, data library, data archive). This repository stores the data sets on which analytics are run to gain insights and predictions around customer behaviour. The business then takes a call on what insights to offer the customer . 

To get to the data sets however is a major task. The different sources from which data will be gathered need to identified. The data from each of these databases will be placed in a data warehouse . To get data into the warehouse requires ETL tools that will extract data from each of the databases, transform the data format of this data  into a format the data warehouse will understand and load the data to create the data set for analytics.

In fast moving real time businesses like banks, the data needs to be current, accurate and changes need to be captured . The processes and technical architecture of the data repository needs to accommodate these requests. Many ways to create these technical infrastructures exist and new ones such as cloud based ,open source, hybrid and connections with social and data feeds add more complexity.

Start small or repurpose an existing data set and scale up as you build consensus and maturity to deliver these data sets.

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The need for data integration

 In modern banking technology environment , the quest to get hold of data is a very complex exercise. First the data about the transaction has to be collected and there are rules on what to collect , how to collect and from where you are receiving this data. Then the need to transform the data into a form that can be ingested ,indexed and visualized by the product developers. After this, the data has to be moved to analytical platforms to slice and dice the data in a way that will make sense to the business teams. Finally the data has to be stored for a period of time . While doing all this the data has to be secure and protected  from hacking or loss.

Data Integration is the discipline of IT that comprises of the practices. architecture and tools that allows the bank to manage their disparate data sets in a coherent manner. This discipline covers all flavors and  types- data ingestion, data transformations, data processing, data pipelines, data synchronization, data virtualization, data fabrics, data engineering, data staging, streams data and data services to name a few.

In absence or lack of process around your data it can quickly overwhelm banking operations and lead to deterioration of services and impact the bottom-line.

Luckily this is a well known problem for which many approaches  and technical solutions are available. The challenge is all these solutions cover some parts of the puzzle and the bank has to figure out what works best for them. Understanding your data lifecycle, who needs access to what data are the key aspects to figure--how to do once a path has been decided is the easier part.

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The Data Management Challenge

 Data data everywhere , not a bit to use  can become a familiar story in banks as our sources of data such as ISO20022 information , internet of things (IoT) with every edge device capable of a payment transaction and scads of video, biometric  and other unstructured data. Banks have done a good job of gathering all this data but very few know where it is or how to use it.

Here are the issues:

-Keeping up with storage and use for consumption by analysts and data scientists.

-Why is the data being collected ? Few know why and how it will be utilized and whether it can be even used . But the pile of data keeps growing because we collect everything.

-Lack of process to understand and index queries. Monitoring this growing pile from different sources also requires  indexing the most frequent data. The risk is these data lakes can turn into data swamps without a rigorous process to manage this aspect

Thank you GDPR and CCPA! By making the consumer a stakeholder in the management of their own data  while imposing real costs to banks for failing to obtain data without proper consent, having poor controls on use of data and if they cannot erase it on request , these laws have given banks the opportunity to rethink their data management strategy.

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Data Security in ISO 20222 with Tokenization

 With increasing use of account level credentials being used for facilitating payments it comes with risk of ensuring safety of the remittance information. Compounded by various real time rails and the rise of immediate payments the new payment methods such as wallets, ecommerce, QR code and  other payment methods have increased the chance of fraud . Given lack of delay in the real time rails and that payments are irrevocable, fraud mitigation is taken on a higher priority.

Tokens , which can be thought of as surrogates for the PAN or primary account number have increasingly been thought of  as viable solution to address the risk of not securing rich remittance data. They use the same format numbers as the original transaction so the processing protocols can do their job while the customer experience is not impacted.

The processor receives a transaction from the customer's bank , maps a token value to the transaction and sends it to the issuing  bank for clearance. Alternatively the issuing bank and send both the token and the transaction to the processor who then sends the account information to the acquiring bank for fulfillment.

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Customer minutes and friction as a metric for Banks

 Everywhere we see especially in ecommerce what used to be complicated method of paying for goods and if required getting refunds used to be fraught process. Nowadays Amazon has shown the way with One click ordering and refunds.

This consumer focus to help make their lives easier while interacting with a machine has been rewarded both by customers and the stock markets . What are some examples where banks have made the difference?

- Transaction alerts on the mobile app 

-Customer authentication on the phone if calling from a registered number

-Servicing via chat if customer is on the online banking portal.

These use cases showcase how to reduce friction by reducing "Customer minutes" i.e. time spent by customers to address their issues. Think of how the internal business processes have been rejigged to permit these outcomes.

ISO20022 rich payment messaging data is a building block for enabling these types of friction reductions in business and the real benefit is in terms of better cash flow and the opportunity to innovate on top of the payment message data set.

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Business in the instant payment world

 Many jurisdictions are working on implementing a low value system on real time rails. Built on ISO20022 payment messaging standards, these promise the availability of instant and irrevocable settlement as well as rich data about the transaction such as invoice details.

Some well established business use cases that will benefit from these new capabilities  where payments are available 24x7  as opposed to time delay for ACH and checks are:

-Ecommerce vendors buying and selling online

-Gig economy payroll for workers for payment of hourly wages

-Emergency relief payments that we have become familiar during this pandemic

The benefit to the receiver are obvious-- money in hand instantly. But the business are also needs to step up in managing this sea change such as :

-How will information on the remittance be stored and reconciled

-If you are receiving funds after hours how will this be managed in an automated manner in your AR.

-What approvals will be in place to release the payment noting these are irrevocable and instant.

Think of the improvement in cash flow with these new tools. Also the reduction in time spent reconciling payments. Paying by installment may lead to new and more revenue.

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CSM Opportunity for Banks

 Given the huge innovation in payment initiation  for consumers its only a matter of time that businesses would ask banks what options are available for them to participate in the instant payment world.

Its not going to be easy for banks. They will have to take calls that may or may not pan out. One area they can start thinking about is the Clearing and Settlement function. There is a history of lag and costs in this area. However there is sporadic innovation as well. Ripple's cross border RippleNet system has shown the potential for reducing costs in Cross Border clearing and settlement.  Swift GPI is showing a method to introduce transparency and tracking in cross border payment flows.

But these are technology based offerings. Regulators in many countries are encouraging development of new instant payment rails such as IMPS in India . China, Japan, Australia, Canada, USA and lastly EU have plans to set up these rails and are various points in their rollout.

By taking calls on which  technology to utilize and what networks to participate in , banks can offer differentiated clearing and settlement-- based on  countries they want to focus on, speed of settlement, transparency of payment journey, SLA based straight through processing STP to name a few.

However the table stakes in all these offerings will be an end to end ISO20022 message compliant network

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Banks cede innovation in Retail Payments

 We have seen the great uptick in new methods of payment initiation by the customer. Examples of these are:

-QR Codes

-Request to Pay

-Pay by mobile phone and other devices using Google or Apple Pay

-Buy now Pay Later or Installment Pay.

-EMT or email money transfer

All these new services are can be grouped as Alternative Payment Methods (APMs). These services are possible by layering an Alias Directory on top of the payer, payer's bank, the network, the payee's bank and the payee. Overlay services delivered via the alias directory improve customer experience at all touch points like POS, online commerce etc. . Where the network is a real time rail, account to account settlement is possible much like a peer to peer network thus reducing interchange costs and delays associated with traditional market infrastructure.

Where are the banks in all this? Nowhere!

All of these services have been delivered baring EMTs by non bank actors or fintechs. Why?

These newer players did not have legacy costs recovery to worry about nor to fight "we have always done it this way at our bank" mindset. They also took the customer for granted.

Now these trends are hitting the banks at the core where they have no place to hide: the large corporates demanding these services and if they don't provide these corporate accounts will shop around. 

 The customer payment initiation battle is lost, so what can the banks do ?

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What is LEI?

 LEI stands for Legal Entity Identifier . This is a 20 character alpha numeric code based on ISO 17442 standard. This code helps in identifying the legal entities in a payment transaction. 

For example in Canada all counterparties to the derivatives transactions need to have an LEI. As faster payments and pay mod initiatives kick  in, more market participants including legal companies, their subsidiaries, government departments, charities will be expected to have this code.

The LEI Common Data File Format v.2.1 is expected to record previous legal names, "operating under", "brand name", "trading as". This clarity is expected to reduce operating cost and false positives.

In future once LEIs are incorporated into the business processes substantial cost savings accrue  in verifying entity during loan origination, KYC refresh, credit worthiness monitoring , compliance reporting etc.

Banks can consider using LEI as a business case to step up the ISO20022 modernization effort.

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