Month: October 2017

Analytics: From Delphi's prophecies to scientific data-based forecasting with the use of analytics

Man’s desire to know what the future holds is nothing new or out of the ordinary.
7th century BC:  In ancient times, such a human need can be first seen and best exhibited by exploring methods the ancient Greeks adopted to answer their questions for the future. Historian Herodotus mentions in his work at least 18 temples (e.g Delphi) having a shrine providing prophecies for public and private affairs. All of the prophecies were vague but in some cases they turned to be true making people believers. Several stories show the confidence that people of that era had in prophecies. One instance where Delphi’s prophecy proved to be authentic was in 480 BC before the battle of Thermopylae, when king Xerxes and his Persian army were plotting against ancient Greece. The Spartans consulted Pythia (Delphi’s priestess) regarding the outcome of the battle. She indicated they were doomed but also prompted them to hear their fate. Doing so all Spartans lost their lives, but created a piece of history and gained immortal fame. It sounds like Pythia was right, doesn’t it?
21st century AD: Using data, numbers, technology and statistics we have now evolved from prophecies and indefinite speculations to scientific data-based forecasting with the use of analytics. Analytics is also well-established within business helping organisations to improve business performance. Data contains the history of your organisation and analytics is definitely trying to tell you something.
Analytics consists of four pillars as introduced by Gartner’s analytics maturity model. To make these analytics pillars sound familiar and applicable to your financial institution, we can give you lots of intellectual or simple (but not simplistic) day-to-day examples. These four pillars indicate how your organization can go up the maturity curve in leveraging analytics in your business.
Let’s take up a scenario where your organization suffers from customer attrition, you can use the analytics pillars to:

  • Pillar I, Descriptive Analytics: As a starting point, you can measure the attrition rate and quantify your losses. You can realise the magnitude of the problem and prioritise it among other business pains you may have to deal with and give it the proper attention.

  • Pillar II, Diagnostic Analytics: This pillar will help you examine a complex topic and decompose it into smaller parts that can be better understood. You can diagnose the most significant dimensions of customer attrition or the parameters that gave it a rise. Such dimensions can be geographic regions, branch ids, the time period of the year, channel types, product types, or customer segments where you observe the highest attrition rate. Using these observations you can learn a useful lesson from the past and you can then apply a relative corrective action in the future. But still, this pillar allows you to take re-active actions, after having first suffered the losses.

  • Pillar III, Predictive Analytics: Analytical predictions won’t produce an unquestionable future statement, but they will help you arrive at what is most likely to happen based on previously observed and statistically validated patterns. This is feasible by exploring and discovering hidden correlations between data, uncovering customer behavioural patterns, market trends, highlighting sequences of events that can lead as a domino effect to what you ‘re trying to predict, or by applying statistical modelling processes like Logistic Regression, Decision Trees etc.

    Predictive behavioural models can calculate the probability (0% to 100%) of each of your existing customers to leave your brand in the next few months, so as to take pro-active actions before it is too late. You can also define a threshold of churn-pressure score which is considered to be high enough to trigger your actions and direct your retention campaigns to, supplying you with a sufficient window of opportunity to retain the customers at churn-risk.

  • Pillar IV, Prescriptive Analytics: This pillar will help your organisation to perform and explore numerous business simulations and assess the anticipated outcome of a certain business scenario you are exploring to apply, before you actually decide to deliver it to market, e.g.
  • How much can you expect to decrease the churn rate if you manage to migrate customers from a traditional channel to a digital one?

  • What is the promotional offer and incentive that can make a customer at churn-risk stay?

  • What is the Next Best Offer you can make to each individual customer?

  • Can services or product personalisation help you retain more customers? How much?

The intelligence derived as outcome of analytics is the right piece of technology to arm your business with. The four analytics pillars can be used to help financial institutions respond to various business challenges, such as customer attrition, customer acquisition, cross-sell, up-sell, customer lifetime value, asset utilization, non-performing assets, fraud-risk, credit-risk, default-risk, reputational-risk, market-risk, performance management and many others.
Analytics can lead to improved operational efficiency, better customer service, more effective marketing, competitive advantages over rival organizations and better P&L statements for your organisation. The objective is always to improve the business by gaining knowledge which can be used to guide decision making, suggest changes, make improvements, recommend next best actions, or even exploit analytics for innovation.
Financial institutions can also expand the use of analytics to benefit not only their organizations but also their own customers, ecosystem partners, or their customers’ customers. In this way financial institutions can become more customer-oriented, create better services and act like truly trusted partners of their customers helping them grow.
While banks understand the importance of analytics, many of them struggle to realize meaningful returns as the initial investment costs are significant. One way to counter this would be to leverage advanced analytics solutions that use banking data models, new-age open-source technologies and built-in intellectual property to rapidly develop actionable insights. This way can make a difference and help you realize high returns on small analytics investments, while discovering meaningful customer behavioural patterns collecting and integrating data from structured data sources (bank’s internal systems), semi-structured sources (ATMs and corporate website navigation logs), or unstructured sources (data coming from social media or other news feeds, machine sensors IoT).
Finally, could we symbolically claim that Pythia was operating in analytics Pillar III? We can definitively spot 3 pillars in the pic below, the Oracle of Delphi…

The Oracle of Delphi

Banking on Analytics

From a sluggish economy to disruptive digitization, banks have managed to survive and evolve along with the current environment. Most of it has been possible due to progressive banks showing the way and adapting to the environment for a truly digital transformation. Of all the major trends for banking transformation, analytics has made a consistent appearance year after year. As the hype surrounding big data and analytics has matured, banks are looking at ways to implement technologies to be more effective in terms of return on investment (ROI) and business value generated from analytics.
I believe that there are three factors – consumers, technology, and insights for all – that will be the drivers for the next wave for analytics implementation in banks:
Consumer expectations and technology advancements will drive banks’ analytics investments
As a result of the rapid digitization, consumers have become used to a fast-paced life and they expect personalized, contextual products or services instantly. The challenge that banks face at this point is that they can no longer depend on descriptive or diagnostic analytics to dwell on the whys. Now banks have to shift their focus towards the “hows” and use fast and real-time data that can predict the consumer journey and provide its consumers with relevant products or services.
With the latest technology in the form of AI or smart devices banks can look to provide personalized consumer experiences based on context and life-events to please even the ficklest consumers.
A simple use-case for personalized customer experience may be in the form of banking apps that most millennials access from their mobile phone to carry out payments and other transactions. When a user logs in, analytics helps the banking application understand what the consumer is most likely to do, and creates a user experience on-the-fly that is optimal for this consumer at this point in time.
Themes based on personas can be another use case for analytics implementation for customer experience.
Analytics helps with understanding the life events of consumers. This is used to determine the optimal user experiences for them at a certain point in time.

  • Technology will be powered by analytics
    It has started to dawn on banks that while analytics offers valuable inputs to humans for business decisions, there is a certain section of technology that benefits from it as well. And insights powered by data and improvement in automation have made sophisticated AI technology easily accessible – more so for institutions that weren’t able to implement it initially due to lack of internal resources and dearth of R&D skills. With analytics and process automation as the driving force behind it, the modern AI platform has the ability to transform traditional banking institutions for the digital era. Progressive banks are already looking to effectively leverage data and advanced analytics modeling that will put them in a position to capitalize on newer technologies such as machine learning and automation.
    For example, Wealthfront utilizes AI capabilities to understand how consumers are investing or spending, and then provides pertinent financial advice to them. Sentient Technologies continuously uses AI powered by insights to create investment strategies for users. Banks such as RBS have implemented AI in the area of customer service in the form of Luvo. It is a smart assistant that supports service agents who are answering customer queries. Luvo can search at higher speeds through a database to provide faster answers; it can also continually learn over time from gathered data to be more efficient with each interaction.
    Banks also have to be aware of the fact that it is not only the internal processes that need a facelift in the digital era.
  • Insights for all – every time, everywhere
    Till recently the insights derived from data were available to only the top management. But with changing customer behavior and technology that is driven by data, it is important that data is made available to all for optimizing internal as well as external processes. This in turn means that everyone should be provided with significant data management capabilities, and a talent base within the organization that will assist in deriving insights from data. For example, a few bank leaders came together to find solutions for difficult policy issues through a crow-sourced effort to use new data sets. The Bank of England has hired a Chief Data Officer, and has established guidelines around the instatement of an advanced analytics group and a bank-wide data community within the organization. It has also created a data lab to understand how these various streams of data can be combined to form actionable insights.
    And it is not only internal processes that can be improved with analytics. A case can be made for making analytics capabilities available for customers too. US Bank’s Payments division had created an application, InfoApp, that allowed their small business customers to analyze their expenditures and other corporate payments. It provided the small business owners with a consolidated view of their finances, as a result of which this app was an instant hit.
    This just goes to show, how democratization of analytics is just another avenue for providing a differentiated and personalized customer experience; and this in turn allows banks to stay relevant in today’s digital world.

Indeed, banks will look towards gaining the competitive edge through investments in big data and analytics. While previously the cost of investment was one of the barriers for enterprise wide analytics implementation, it no longer is the case with open source technologies, such as Hadoop. As these open source stacks bring down the cost of investment, banks will start to see effective ROI with these investments in analytics. But it does not end with investments in analytics alone; banks will have to cultivate a robust, analytics driven culture within their organizations and foster a bent of mind that will be insight driven. The success of these implementations will of course depend on the competent execution of technology, employee empowerment, and democratization.

Bots: What’s in it for banks?

The growing interest of banks in the BOTS technology from a customer viewpoint can be largely explained by evolving preferences. Today’s digital users are well connected and have tons of information but what they want is timely, useful and non-intrusive advice and analysis.
Organizations have been chanting the automation, personalization, and digitization mantra for a while now and chat bots clearly fit that story. Used effectively, they can bring significant cross/up-sell opportunities and cost benefits.
Automation, AI, customer experience are the buzz words that banks live by today. BOTs provide banks a happy confluence of the three – enabling automation of tasks through AI, while providing exceptional customer experience.
Here are some of the reasons banks need to look at BOTs –

  • Engage customers and prospects
    Today’s customers prefer chatting over speaking to a human executive, and emoji’s over words. They want to communicate on their own terms and at a time of their choice. Banks can take a cue from these pointers and start engaging customers through chat. A more engaged customer will mean more business. Several banks have now understood that they have to meet the customer where the customer spends most time, in place of expecting customers to open banking apps to do their banking. We are noticing a slew of banks launching chat bots on Facebook messenger and Twitter. Examples include MasterCard and Yes Bank. With chatbots], banks ca given a contextual customer experience that makes banking frictionless. A good example for this is DBS which has introduced a chatbot based on Facebook messenger.
  • End to end value chain access
    Customers want to know more than their account balance; for instance, they may like to know how much more they can spend without overstressing their budget. To this end, a bot can play a role of an aggregator to serve as a one stop shop for all customer queries. For example, such an aggregator bot can in turn call the expense manager bot and so on. Overall, the bank profits from automation and customer centricity brought in by this.
  • Automation tool
    Bots, being able to take actions on behalf of customers and their employees, are of significant interest to banks. To enable this, chat commands from users are tied at the back-end to trigger specific actions. An example of this is that a chat request for stopping paper statements should trigger the relevant instruction to the reporting system. This should then be followed up by relevant questions based on the availability of customer information for electronic statement delivery.
    Additionally, BOTs can help banks in automating their internal processes. JP Morgan uses a Bot to streamline its back office operations, which helps in handling common IT questions like resetting passwords.
    BOTs deployed internally to help bank staff with faster and better connectivity with other important functions such as Finance, HR and administration. It also provides better access to the bank’s existing knowledge base.

Bots have the capability to connect the dots and enable the banks to expand their ecosystem and provide a truly meaningful and profitable digital experience. Hence, we recommend that banks should start their journey with BOTs by identifying a good a business use case where they can provide the differentiation.

New Principles to Explore Banking Blockchain Use Cases Effectively

A new breed of blockchains are being developed to become identifiable, controllable and asset-agnostic. This evolving architecture demands new rules to explore use cases in the financial industry. Here we outline three use-case principles for bank CIOs that can potentially guide their blockchain investments in 2017.
FIs are cautious by nature. Blockchain technology has the potential to revolutionize financial transactions but several challenges have to be overcome – one of the fundamental question from FIs is where to start for ‘proofs of concept’ of blockchain use cases.
In 2016, Moody’s published a whitepaper that identified 25 important use cases from over 100 use case candidates, most of which are still valid in 2017 – but a collection of 25 use cases is still far more than sufficient to give a clear view of exactly which use case fits most to a bank’s business. The new blockchain architecture – identifiable, controllable, and asset-agnostic – demands FIs to explore further what are the right use cases. In this chapter, we introduce three fundamental principles to help identify best-fit blockchain use cases, so that FIs can move forward to operationalize the technology and integrate it into current processes and systems.

Principle #1: Non-Monetary

In an era of cybercrime and stringent regulatory requirements, blockchain regulation is a gray area in the financial industry, creating some uncertainty around its implementation. Regulators are keeping an eye on blockchain technology, especially in the area of:

  • The basic tenets of a KYC/AML compliance program (for instance FINRA 3310) require the customer identifies (who, where, and which organization) must be traced by blockchain payment system
  • The general ledger integrity must be assured – all distributed ledgers that reflect account changes must be reconciled to bank’s enterprise GL

In such a context a blockchain, when tied to fiat or virtual currency, creates extra complications with compliance and legacy integration. Instead, documents, records, financial instruments and even private equities carried by blockchain could be an ideal way forward to avoid the compliance hurdles and regulation uncertainties.
However, central banks are catching up quickly. In countries where regulation supports blockchain innovation – for instance, the Monetary Authority of Singapore is testing blockchain for a new payment transfer project – FIs could adopt an aggressive approach to try blockchain payments in sandbox environments.

Principle #2: Contained

R3 CEV, as one of the largest blockchain consortium, stated early on, that the initiative will, ‘seek to establish consistent standards and protocols for this emerging technology across the financial industry’.
However, the question for financial firms is whether they should work together on a standardized consensus model that everyone agrees on or work independently on different versions and let the market decide. The other side of the coin is it takes time for a consortium to reach an agreement, and extra efforts to implement – any initiative of this scope to harness a standard in a fast pace environment is definitely not easy, and sometimes a road block not a catalyst to innovation. What happened to R3 in recent year has shown the difficulties of building an ambitious consensus among a large group of key stakeholders.
Therefore, FIs could first look at use cases that could be implemented inside the organization, or within a much smaller scale of consortium – it enables them to test the technology in a time-to-market manner, and start small with niche-market use cases. Most FIs are interested in blockchain but have not participated in major consortiums – for instance, regional and small-medium-size banks. Such FIs can go ahead with this light-weight approach, and even global multinational banks could run a bi-model strategy to try both contained and consortium relevant use cases.
Having said that, a bank should consider all these factors in a local context to determine the best-fit approach. There are exceptions where agreement can still be efficiently made among a group of banks. It could be achieved by a powerful centralized organization, for instance, a financial holding company that includes a family of banks, or a strong state-owned banking association that represents a group of local credit unions.

Principle #3: Distributed

It is essential to understand that from a business perspective, blockchain doesn’t fundamentally change how payment or money market works. However, blockchain as a technology platform can be adapted to a business context – and potentially transform the whole process and operations.
The dispute of the pros and cons between a centralized database and decentralized database has been continued for decades. Centralized system has approved its efficiency in many business scenarios to handle mass volume transactions, for instance, core banking. On the other hand, although no formal benchmark released to indicate any serious capacity issues for blockchain, efficiency (for instance network scaling problems) is a general concern to not just blockchain but many other types of distributed databases – where significant overhead or replication associated with these protocols exists.
However, blockchain, by its nature as a distributed record-keeping database, could demonstrate its true value, especially in a collaborative, distributed business environment. Typical scenarios include trade finance, international remittance, and syndicated loan. For these use cases, and with careful planning, FIs should have the confidence to deploy a scalable blockchain solution not only in a sandbox environment but also in a production environment.

The Digital Imperative

The migration to a digital banking world is expected to be a rough drive and likely to lead to further fragmentation in financial service markets. This digitization of financial services will be accompanied by a significant shift in power and influence from existing financial services providers to other intermediaries and customers.
How do banks evolve to meet the emerging challenges and expand their role beyond traditional financial activities? How do they fit into the ‘brave new world’ of Banking-as-a-Service (BaaS) and partner with banking aggregators to deliver personalized services to customers in real-time based on what they want and not what can be offered? The time has come for a new wave of Automation – quite different from the heady days of bank mechanization or computerization. BI and Analytics will bring in the next wave of differentiation. There will be increasing use of API-based aggregator ecosystem where each partner of the conglomerate performs optimally based upon inherent core competencies to deliver value and enhanced customer experience (CX) and generate and optimize revenues for the banks.
An evolution like this requires acquiring and mastering new tools, developing robust technical foundations, and devising new strategies for business growth.
The roadmap for adoption and mastering of new tools is expected to follow the following implementation strategy for a successful adaption to the new business models:

MEDIUM TERM: TECHNOLOGY ADOPTION

In the medium term, the following technologies must be adopted:

  • A customer omni channel experience platform (consistent, cross-channel, etc.).
  • Marketing and CRM based on advanced analytics and leveraging existing and newly available customer data for targeted offerings.
  • Open platforms and APIs to participate in an extended ecosystem, to deliver services and to provide an offering beyond traditional financial products.
  • A social and collaborative platform, leveraging communities (either existing or bank-created) to interact, exchange information, advise, check peer opinions, use the community’s knowledge, etc.

This enables banks to gradually step into new business models, co-operate with new players, and adapt to new customer expectations and behavior. In this way, banks can achieve significant results in securing market share and growing their business.

LONGER TERM: TRANSFORMATION PROGRAM

From a longer term perspective, a deeper transformation of a bank’s culture and organization is required to achieve the full potential of digitization. In particular, mastery will be achieved by those who make full use of data to drive their business and marketing strategies and pursue innovation beyond existing banking practices.
Main focus areas include talent acquisition, culture and IT transformation.

  • Talent acquisition
    Organizations are struggling to find the right talent in areas that cannot be automated. Such areas include digital skills like those of artificial-intelligence programmers or data scientists, and of digital marketers and strategists who can think creatively about new business designs.
  • Culture
    In the digital age, a bank will put innovation at its core, which will drive the development of new services and offers and create competitive advantages.
  • IT transformation
    A business transformation must be supported by a corresponding IT transformation.
    Investment prioritization should thus establish a robust technical foundation for digitization, including customer communication solutions, cyber security, collaboration tools, storage technologies, analytics, modern core systems, and risk management.
    As such, the transformation program should create the agility, flexibility and openness necessary to thrive in the digital world. This can be achieved by decoupling the production, offering, and customer interaction layers. These layers can then feed and build on a data core (comprising customer data but also data from the entire ecosystem). In the digital age, data will be at the heart of every business activity, and a competitive advantage in its own right.
    Banks are in various transient stages of mechanized to digital (internet and mobile) to a truly digital model (Robotics, Analytics and AI). AI and Machine Learning algorithms are increasingly used to self-learn and predict outcomes to enhance the customer experience. Digital is a journey, not a destination. Banks like other businesses must continuously and relentlessly evolve at higher velocities to meet the challenges of the digital age.

The New Era of Blockchain – Innovation made Usable

Blockchain is fast coming of age in the banking and financial services industry and its potential can be fully leveraged by selecting the right use. This sentiment is echoed across industries, with many large banks looking to exploit it for multiple use cases. However, blockchain in its true avatar may not be entirely suitable for highly regulated financial institutions. Hence, a new breed of blockchains is cropping up to meet financial institutions requirements.
Financial institution’s (FIs) priority has moved from understanding blockchain’s potential to what further needs to be done for a blockchain to work within the regulatory boundaries of the industry. Their aim now is to meet the key aspects that a financial institution requires in order to use it effectively. Three broad areas are emerging in the ‘New Era’ blockchain architecture.

Identifiable

  • The pseudo-anonymity of the blockchain does not work for FIs, where ‘identifiability’ is key

  • To meet regulations like KYC/AML and FATCA, FIs need to be able to identify and pinpoint the identity of the nodes

  • This is addressed by including the ‘real world’ identity to the blockchain address based identity

  • ‘Auditability’ was always a strong point of traditional blockchains, but with ‘identifiability’, it gets stronger

  • FIs can take this further and build a fool proof repository of identities on the blockchain

Controllable

  • A public blockchain gives very little control to its participants and is truly democratic in nature. This does work in many cases as FIs need to be able to exercise control on several aspects, especially in the financial regulation space. Therefore, the need and the proliferation of private networks (or consortiums)

  • Such networks allow control to a group or an individual organization to decide who can participate, how consensus on viewing rights, membership and frequency of validations can be arrived at

  • Such a permissioned environment brings in certainty, predictability, and accountability into the blockchain based system

Asset Agnostic

  • The concept of creating value on the blockchain has been a point of controversy from the time of its inception. Regulators have mostly taken a negative stance on the ‘value’ aspect while being open to the blockchain itself

  • Thus, a blockchain which is not tied to one asset (that is created on the chain or otherwise) is an ideal way forward

  • Such chains allow FIs to transfer legal tender/instruments/documents or any other asset which can be digitized

These adaptations of the blockchain make it extremely attractive to the financial industry. The FIs can now focus on the use cases that interest them most, rather than worry about the suitability of the underlying technology. Key considerations for FIs to look at for developing use cases would be regulatory constraints, the readiness of the ecosystem and of course the nature of the process/area that is under consideration.