Commentaries and insightful analyses on the world of finance, technology and IT.

February 22, 2021

Re-imagining bill payments

Payments - the 'holy-grail' of the Fintech has seen big disruptions in Person to Person (P2P) payments bringing speed and convenience to customers with the likes of commercial payments and bill payments still waiting in the wings. It is easy to understand why. Consumers are in both the ends of P2P and consumers adopt innovations faster than the enterprises and accessibility is easier. Venmo set the trend and soon others followed, notably Zelle which is creating a niche in the P2P payments space. Zelle provides near real time, email/mobile number based payments, and is driven by a good funds based credit push model - the key selling points for Zelle. The Zelle network is set to expand with more banks and credit unions joining it and will be a major player in the P2P space. 

Another important development is the TCH RTP that will allow banks to provide real time payments and make them compete with the Fintechs. It will be interesting to see how TCH RTP and Zelle, which itself is from a company owned by large banks, can compete. It is quite possible that Zelle integrates with TCH RTP eventually. Recently, Federal Reserve Banks announced the development of FedNOW - an instant payments service for banks. Such developments are transforming the P2P payments and very soon the question will be - when will such transformation can come to commercial and bill payments, where  intermediaries a.k.a billing service providers have a major role to play. 

There are some important facets to learn from the success of Zelle and the way TCH RTP is designed that can offer disruptive solutions to bill payments. Here, we look at systemic issues with the current design of bill payments and what changes in future will disrupt the space.

Debit origination
The bill payment process is fairly simple in the case where the customer maintains a checking or savings account with the Payee - say, customer has a credit card account and a checking account with the same bank. Both the debit and credit part of the payment can be quicker, and it does not require any overhead reconciliation or settlement mechanisms. But the process becomes very complex when the customer wants to pay from an account with another bank or when there are other intermediaries involved in processing the bill payment. In such cases, the biller or the intermediary relies on debit origination, something like an ACH Debit instruction that is first sent to the bank from where the customer wants to make a payment. It is not possible to get the status of the debit transaction in real time, it can fail, and needs batch processes to reconcile and settle the payment making the whole process complex and taking multiple days before completely closing the transaction. The prevalent model is like a digital version of the same process when customers used Checks to make bill payments in the past. 

Good funds based credit push model 
A shift from debit-pull to a credit - push model will be a game changer. Bill payments should be designed to work on a good funds based credit push model - an approach that laid the foundation for the success of the Zelle and the TCH RTP approach. The model reduces the risk by limiting payment failures which in turn eliminates many process overheads and is also real time. The same approach can ideally become the backbone for how the bill payment systems must evolve in the future.

Real time posting and notification
A major benefit of designing around a good funds based credit push model is that the debit on the customer's account can be verified immediately and that will support the real time posting of the bill payment and notification of the payment status to the customer. 

Often it is pointed out that real time payments have risks particularly fraud risk and that it does not give enough time to reverse the transaction. But this fear cannot be an impediment to faster and simpler payment processes. The initial success of the real time P2P payments over the last few years and the payment infrastructure modernization by TCH RTP and the likes of Fed NOW is paving the way for building disrupting payments apps and services. New payment models will soon arrive in non-P2P payments like Bill payments. A good funds based credit push model will be the guiding factor for these emerging payment systems.

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December 22, 2020

Excavate opportunities around Disparate FinCrime case management systems!

Alert and Case management is the spine of any Financial Institution to effectively tackle financial crime activities such as fraud and money laundering. It is vital for FI to be compliant with regulatory requirements and avoid any reputational damage. I happened to deep dive into the FinCrime case management landscape recently and thought of sharing my view with this blog.


When drilling down to FinCrime case management solutions, one of the major bottlenecks for the effective and efficient decision-making process for investigators is

  • Disparate IT systems for each LOB or departments causing unavailability of required data for investigation in one place

While there are many challenges around case management solutions, I would like to focus in this blog primarily on disparate IT systems.


I think even top financial institutions struggle to overcome this challenge as

  • mostly it requires an IT transformation of their much-acquainted legacy systems where reluctances from the business for the change
  • political challenges for aligning all required stakeholders across LOB
  • budgeting constraints and so on

However, FIs must integrate their siloed systems and create unified enterprise-level case management and investigation platform. This could be an adventurous move, but to stay relevant in the industry among competitors and tackle ever-growing financial crimes, this transformation journey is vital.  


To address this challenge, I have seen some FIs decide to build their in-house solutions. The advantage of an in-house solution is that the expense is limited to building the own solution from scratch and subsequent maintenance. But the challenge is on continuously scaling the solution to the latest regulatory needs, keeping in par with the latest technology advancements and R&D spent.


Alternatively, there are multiple market leaders ready with apt industry-proven out of box solutions leveraging cutting edge technologies, automation, AI/ML solutions features, and easier system integration through APIs. The advantages are no investment required from FIs on R&D, continuous scaling of the solution aligning latest technological advancement. However, the challenges are the dependency on the third-party vendor for of out of box product-related fixes and limitations, EOL related issues, and in some cases hefty licensing cost.


Largely, considering the pros and cons, I feel, most of the FIs inclined to go with third-party solutions with proven market leaders. 


NICE Actimize is one such market leader in fraud and AML case management solutions. They are providing unified case management solutions that can be quickly and easily integrated with interfacing systems. They have elevated their RCM/ECRM solutions by augmenting RPA, AI/ML capabilities to further bolster and provide a unified alert and case management solution which is called NICE Actimize ActOne.


Some of the ActOne features such as Automated Entity Resolution, advanced automated work allocation to eliminate manual work allocation, productivity studio to measure operational efficiencies, RPAs for advanced automation, and process consistency are some of the key differentiators that will help address operational inefficiencies resulting in significant productivity improvement and cost savings.

 

Needless to say, a variety of products and solutions such as Pega, Appian are available and FIs can choose the right one that is most suitable for their environments by doing specific market research.

 

While some of the customers, already leading their journey towards advanced unified case management systems, there are still many customers at the early stages where there are opportunities to pitch in proactively and recommend solutions. And it's the right time to excavate for such opportunities with your customer landscape!

 

Feel free to share your view and experience on similar case management products and solutions.

 

 

References:

 

www.niceactimize.com

August 28, 2020

Cognitive cloud lending: Intelligent. Fast. Accurate.

There have been numerous technology trends over the past several years; some turn out to be mere hype, while others deliver tangible returns on investment. Identifying the winners is a priority for all businesses as they evaluate where to focus spend.

 

This is especially true for financial sector businesses such as mortgage lending. Artificial intelligence (AI), natural language processing (NLP) and machine learning (ML) are some of the more recent technologies that are solving critical business challenges and delivering ROI to early adopters. But can the interplay of these technologies unlock even greater value?

 

Cloud + AI = Cognitive cloud lending

 

Marrying AI with the benefits of the cloud will create cognitive cloud platforms that can have far-reaching impact for mortgage companies. Cognitive cloud lending platforms and solutions can provide lenders, borrowers, brokers, dealers, real estate agents with more self-service and transparency options throughout the lending experience. Further, they can extend this ecosystem, making it possible to create and bring cognitive computing applications to the masses.

 

An apt example here would be the Mortgage Cadence Platform. It is the only comprehensive single system of record technology which is extensible through an open API layer, allowing banks/lenders to connect with third-party providers of their choice. It also offers valuation, verification, title, credit, fee, and compliance services. Another example would be Cloudvirga, the firm behind the cloud-based intelligent Mortgage Platform® (iMP). Designed to streamline the mortgage process, this platform is digitalizing the mortgage industry by deploying automated workflows to reduce cost, increase transparency and shorten the time it takes to close a loan for both borrowers and lenders.

 

Four ways to enable cognitive cloud lending

 

1. Replace monolithic software solutions - To get projects up and running, most large companies use monolithic IT architecture as it is faster to set up. However, as the system grows, the code and architecture become more complex and more developers are needed to maintain these. Companies also lose critical speed, flexibility and agility to respond to the market. Organizations embarking on digital transformation should consider an agile style of application architecture that enables rapid delivery of new cloud-based digital services.

 

Microservices architecture is one way of achieving this. This architecture encapsulates business entities that orchestrate multiple business entities as in the case of credit evaluation. For example, microservices can support and automate an existing loan origination system. It can accelerate risk decisioning processes in a secure manner whereby risk scores are calculated within a second of the application being received. The result? Applications can be approved in a fraction of the average 'time to cash'. To put this in context, the average time to cash is 40 days and even top performers take 18 days.

 

2. Encapsulate the core with process APIs - Instead of designing infrastructure around applications, service-oriented architecture (SOA) focuses on specific services. SOA is very useful in supporting business processes without having to worry about the underlying applications. Process APIs act as the communication layer, combining data from disparate sources and making it easily discoverable. For mortgage lenders, using process APIs brings about visibility, programmable services and greater agility.

 

Faced with numerous requests from customers looking to switch mortgages, a US-based home mortgage lender decided to automate its loan underwriting system. By leveraging SOA, the mortgage company can employ reporting tools and follow industry best practices, all while managing loan applications seamlessly.

 

3. Leverage cognitive BPM for intelligent automated services - Cognitive business process management (BPM) refers to the new BPM paradigm enabled by cognitive computing. It encompasses all the typical BPM aspects in addition to three transformational aspects: Firstly, CBPM will drive knowledge acquisition at scale, enabling knowledge-intensive processes (KiPs). Secondly, a 'plan-act-learn' cycle will emerge as the new process meta-model. Thirdly, the models can study process descriptions both during design and run-time. This will open up opportunities for new levels of automation and business process support for both traditional business processes and KiPs. A research paper on CBPM hypothesizes that cognitive computing will leverage AI to manage dynamic processes, achieving intelligent automation, continuous improvement and higher performance.

 

In the mortgage industry, cognitive BPM can be useful to determine customer satisfaction. Let's take an example, when a customer's loan gets approved, he/she is directed to the bank's loan servicing department. This department monitors changes to the payment plan and ensures proper payment collection. Its operations include outbound and inbound calls that generate call transcripts. Now, if the bank/lender applies cognitive analysis to this process, it can determine whether its employees are asking the right questions, how efficiently are they working, how polite are they with the customers, so on and so forth. Based on these insights, the bank can take actions to either improve customer communication or leverage new ways to improve customer satisfaction as well as the bank's profitability.

 

4. Connect front and back-end systems - To improve lending and advisory capabilities in future, banks will need to harness low-code applications and automation technologies that foster connections between front-end and back-end systems. This will also support smart case management, a capability that allows adaptive business processes In a post COVID-19 scenario, this kind of agility can mean faster loan disbursals for SMEs seeking financial stimulus from their respective governments. In the UK, nearly 69,000 loans were approved within 24 hours of a 100% government-backed scheme, highlighting the mounting pressure on loan processing teams. Using APIs to facilitate back-end integration promotes reusability as it unlocks new opportunities to integrate with other front-end technologies like chatbots.

 

Intellect Design Arena, a financial technology provider, built the Intellect Digital Lending (IDL) 20 on an 'always on, always aware' concept. Designed on a do-it-yourself (DIY) principle, IDL 20 allows banks to create their products anywhere, anytime. Banks can also make real-time informed credit decisions. It also enables banks to deliver a real-time solution to their customers by giving them access to a 360-degree view of their credit portfolio. Its fully automated robust architecture reduces operational costs by increasing efficiency.

 

Conclusion

 

The mortgage lending business needs to leverage new trends driven by technologies like cognitive cloud that can accelerate lending processes, making them intuitive, smart and relevant to customers. It is up to banks to align these technologies with the business and develop innovative lending models that leverage the power of intelligence.

March 30, 2018

Innovations in the Blockchain pipeline for 2018

It's hard to believe that, just a few years ago, cryptocurrency was viewed as a fad with Bitcoin being the new kid on the block. Only a handful of people could have predicted the degree of disruption that was in store: By the end of 2017, there were over 1400 cryptocurrencies in circulation with a total market value of over US $500 billion! Today, Bitcoin itself has a market valuation of over US $200 billion.  

While we have Bitcoin to thank for giving us blockchain, the technology has evolved significantly since its nascent years. Today, everyone is talking about blockchain, predicting its growth and looking for ways to leverage it. Blockchain-driven innovation is mushrooming across industries and we can certainly expect it to have a significant impact on the financial services industry. 

Many financial services companies are concerned about public blockchains such as Bitcoin and Ethereum because of issues related to permissions, governance, privacy, transparency, performance, and throughput. In my view, this calls for strategic collaboration between established players and start-ups to develop innovative solutions either on available platforms or using new protocols. The value to be gained through disruptive start-ups cannot be underscored enough. Consider how the equity investment in blockchain start-ups grew from US $98 million in 2013 to nearly US $1 billion in 2017[1].

The good news is that many innovative solutions are already underway. In 2017, Ripple and 3 banks deployed a cross-border payments service for corporate payments and retail remittances. The IZNES blockchain platform for fund administration developed by SETL and four asset management firms went live in January 2018[2]. This year, we can also expect a leading American post-trade financial services company to implement a blockchain solution for credit default swap settlement[3]. Finally, the B3i consortium will also go live with a blockchain solution for property catastrophe excess-of-loss reinsurance contracts. 

Blockchain helps companies streamline processes such as reconciling data across various market participants, auditing records for regulatory purposes, authenticating counterparties, verifying origin of transactions, and reducing record duplication across businesses. However, its greatest benefit is the staggering operating cost reduction that it delivers. The above mentioned project by the American post-trade financial services company has already reported savings of 20-30%, the B3i project for reinsurance contracts has achieved productivity gains of up to 30%, and an industry-wide commodity trading project reported 33% increase in trader efficiency. 

Clearly, blockchain has a lot to deliver to financial services companies. So, why aren't we seeing an increase in adoption? The reasons for this are many. For blockchain to work, there should be collaboration between multiple participants across the value chain. Some companies may face technology-related issues when implementing blockchain across their landscape. Further, the general lack of understanding about blockchain and uncertainty about its return on investment can lead to poor management buy-in.

So, if you are a financial services company, here's what I recommend:
Stay updated with new developments in blockchain
Ensure that senior management is up-to-speed on developments
Study your business processes to understand which ones are likely to be impacted by blockchain
Explore partnerships with other financial services companies
Develop a broad range of internal capabilities around blockchain technology

A look at some of the emerging use cases for blockchain reveals how it can be leveraged across the banking and financial services industry. For instance, EdgeVerve, an Infosys company, is partnering with 11 banks to create a permission distributed ledger system that simplifies and accelerates the adoption of blockchain for banks[4,5]. Infosys has also published the Blockchain Revolution Report that provides deep insights into the applications, value and impact of blockchain. The report is a useful resource for companies looking to further their understanding of this innovative technology. With the right understanding, strategy and insights, financial services companies can position themselves to win big and sustain their edge with blockchain.

References

1. Blockchain Investment Trends in Review", CB Insights, October 2017
2. SETL and 4 asset management firms are launching IZNES, pan-European fund record-keeping platform based on blockchain technology",
        SETL Press Release, September 2017, https://setl.io/
3. DTCC Selects IBM, AXONI and R3 to Develop DTCC's Distributed Ledger Solution for Derivatives Processing", 
        DTCC Press Release, January 2017,
        http://www.dtcc.com/news/2017/january/09/dtcc-selects-ibm-axoni-and-r3-to-develop-dtccs-distributed-ledger-solution
4. Reference: "Infosys Finacle Launches Blockchain Based Trade Finance Solution", Infosys Press Release, November 2017
        https://www.infosys.com/newsroom/press-releases/Pages/finacle-trade-connect-solution.aspx
5. DTCC Selects IBM, AXONI and R3 to Develop DTCC's Distributed Ledger Solution for Derivatives Processing", 
        DTCC Press Release, January 2017, 
        http://www.dtcc.com/news/2017/january/09/dtcc-selects-ibm-axoni-and-r3-to-develop-dtccs-distributed-ledger-solution

September 18, 2017

Potential of AI in Fraud Detection

Technological innovation is the new normal in today's world. How many would have imagined a drone could be used for advanced pitch analysis in cricket matches, which has been tried out at the recently concluded Champions trophy in England. Though Artificial Intelligence (AI) has existed for decades yet it is being widely accepted and tried out in the Banking industry now, more than ever. In the Banking Industry, AI is already making inroads be it in Customer Service like introducing Chat-bots, or in AML, or be it Fraud Detection. AI has been and is helping Banks in being more proactive in their approach, like identifying a potential fraudulent transaction even before it happens.


What Potential does AI hold in fraud detection for Banks?

Banking is one industry that deals with a huge amount of data, which could be leveraged to do advanced analysis and come up with meaningful insights. There is a range of activities being carried out by banks in using AI for customer service, for personal financial services, etc. However, there is huge scope for banks to utilize AI techniques in fraud detection. For example, Banks have started using AI techniques in identifying the fraudulent transaction patterns in a card and use the data to prevent frauds. Banks could have a software which could raise a red flag if a customer has accessed his account from 7 to 8 different IP addresses in a span of a week, however the customer could be an artist/actor/tourist who could be doing shopping while he is travelling. So here, an AI software would be vital in looking and analyzing the spending pattern closely. The AI technique here makes the machine to think like a human. Another potential use of AI would be to analyze the user profile based on transactions done and then try to determine whether there is reasonable suspicion. This way the Banks can avoid a fraudulent transaction even before it occurs. Some banks have already started replacing passwords with voice recognition for some of their services which uses AI. Now, this can only be achieved if the system is fed with the historical data of the fraudulent patterns as well as the historical data of verified transactions. Recently MasterCard announced an AI fraud detection service, which helps the FI's reduce the false decline and increase the accuracy of real time approvals for genuine transactions. This would be a great relief for customers. Nothing is more frustrating for the customer than to have their transactions declined for no fault of theirs. The use of AI, which could analyze their spending pattern in order to identify fraudulent transactions, would be truly beneficial to the banks in better understanding their customers. Having said that, every technology comes with its own limitations and that should not deter the banks from trying out AI for fraud detection.


Banks that do not keep updating or taking steps to track suspicious transactions are at a greater risk. Banks have been slow in adopting AI for Anti-fraudulent activities. And it is just a matter of time before more banks adopt the AI techniques for not just fraud detection but aversion too.


September 14, 2017

Future of ADM in the Digital World

Background - Technologies Enabling Digital Transformation


There is a lot happening in areas of robotics, Artificial Intelligence, machine learning, and IOT as businesses turn digital and there is much discussion around how future IT would look like with fast evolving digital landscape. Application development and maintenance (ADM) engagements have been mainstay of IT outsourcing. This blog covers how ADM engagements landscape would change in the wake of digital transformation.

Before one makes an attempt to chart out future of ADM engagements in the next decade, it would be good to summarize the futuristic technologies that have been enabling digital transformation.


  • Pervasive Technologies such as Connected Autonomous Vehicles (CAVs) and predictive analytics enabling customer experience.

  • Cognitive Intelligence and Machine Learning are being applied to enable many business and technology functions. Future applications will be built with intelligence to learn and change cognitively, rather than execute on fixed instructions.

  • Wearables and fashion electronics have become popular and businesses offer multi-channel customer connect across various channels including wearables.

  • Disintermediation platforms have removed the middleman and have provided direct connect with the new partners.  Blockchain technology is providing a secure platform for partners to conduct business seamlessly.  

  • Robotic Process Automation (RPA) is quickly changing the concept of workplace.  The future workplace would offer increased co-existence of the robots, virtual personal assistants, conversational systems and humans


Application Programming Interface (API) & Micro services, Data Analytics and Cloud are changing the technology architecture of IT systems.

From Financial Service perspective, Banking on Cloud, Blockchain, Fintech, and Open Banking are trends shaping the industry.


Changing Expectations of Our Clients

In the digital world, clients value a combination of domain and technology skills, and are focused on outcomes rather than wanting to pay based on effort spent.  In this context, the service providers would need to -


  • Offer outcome management capabilities as against pure technical capabilities.

  • Offer single interface to deal with Business Technology rather than providing multiple experts to clients to reach out to. For convenience we call these professionals as "Business Technologists" as against "System Analysts" who brought in IT architectural view or "Business Analysts" who brought domain/industry view.


Changing ADM Engagements Landscape


Discrete design, development and delivery methods will fall short for Digital "Business Technology" projects.  The business technology engagements would require "Design" thinking.  "Design" thinking would remove the boundaries between software and infrastructure development. "Design" thinking process is more customer centric and iterative. It would assist in developing creative solutions when the problem itself is inadequately defined. 


Digital projects would be perpetual in nature and would require ongoing development.  Maintenance work would shrink. 

Digital Enterprise applications will be multi-layered. The schematic representation provides a snapshot -


Front-End

  • Increased self-service layer with applications such as mobility solutions, future branch

  • Limited assisted service applications that can work with third party applications.

  • Third-Party applications such as "Open Bank"

Security Layer - Enterprise security framework that can control access at organizational level

Applications Layer Customer Relationship Management (CRM) and Business Process Management (BPM) layers 

API Layer - API layer and Enterprise Service Bus (ESB) connecting the Applications (CRM, BPM) with products/Services layer.

Products/Services Layer -Products such as Core Banking Platforms, Payment Engines, Anti-Money Laundering, Loans packaged products as well as products offered on Service are part of this layer.

Enterprise Data Lakes - Offers common data across above layers

IT project delivery would follow Agile/DevOps principles - design, development, testing, infrastructure and deployment would preferably come from a single self-organized team to deliver projects.  Cloud native applications can be developed easily using containers/micro-services.  Containers would bridge the gap between new services and legacy applications.

There is a myth that the legacy applications cease to exist in light of new Digital applications.  The legacy applications would continue to co-exist along with new digital applications as customers need to retain the existing IT while they introduce digital transformation. 

The three plateaus of IT that the businesses witness would continue to exist, but the proportions would see a shift with legacy applications maintenance getting optimized, and making way for ongoing modernization and more development.  Digital development of today, considered complex would become the business-as usual.



Legacy is a relative term.  What is "Legacy Modernization" and "Digital" today would become Business as Usual "Legacy" in near future.


Conclusion - Vision for ADM Engagements - Key Priorities


Based on changes in ADM landscape discussed above, some of key priorities include -


Broaden and Deepen ADM reach - Examine each portfolio against our experience/service offerings and penetrate in white-spaces through extreme offshoring and extreme automation propositions. Distributed agile and SAFe methods should be deployed to enable extreme offshoring.  Extreme automation should be considered across onboarding, transition, development, production release, maintenance, and production support.  Also, areas such as Augmented/Virtual Reality (AR/VR), though sound engineering oriented, would easily leverage the programming skills in ADM team and be of interest to our programmers. 


Upskilling/Reskilling - Technology adoption, learnability and understanding of Business and technology is critical for ADM than ever before. Business analysts would need to be well-versed with technology and Developers, Testers and Managers would need to be more well-versed with business/domain knowledge. This expectation fully aligns to Infosys Zero Distance Philosophy - Every developer, project manager, analyst and architect should be at "Zero Distance" - to the end user (Desirability), to the underlying technology (Feasibility) and therefore to the value (Viability)".

The trainings should be on-demand and can be leveraged through partnership.  In addition, there should be increased focus on certifications/skill assessments as part of the training. 


Introduce Infosys Artificial Intelligence Platform (NIA) to ADM clients - Creating a NIA power-programmer team is a key priority.  Programmers should be upskilled in Machine Learning and Artificial Intelligence areas to implement NIA for ADM Clients. Some of the FS-ADM specific use cases of NIA include -


  • Understanding customer purchase behavior across retail channels

  • Data relating to credit history, KYC, fraud prediction, customer churn prediction, etc.

  • Analysis of tax relief at source and exception reports

  • Policy document knowledge and multi-channel chat-bot support


Roll-out Nextgen Delivery Model (NGDM) to enable at any scale, closer connect with clients and teams, enable multi-shift/multi-zone presence with clients.  The computing infrastructure could be state-of-the art with all machines web-cam enabled.


Offer multi-channel and chat-bot support - ADM service line should create a platform for multi-channel/chat-bot support for rapid development and deployment across multiple platforms


Author:
Renu Rajani, Vice President, FS ADM, Infosys Limited.

Supporting Authors:
Sastha Prasad Viswanathan, Group Project Manager, FS ADM, Infosys Limited.
Viral Thakkar, Senior Principal Technology Architect, FS STAR, Infosys Limited.

July 4, 2017

Blockchain in banking: Bitcoin and beyond

'Blockchain' has become the buzzword in the financial world since the time it debuted in 2009. Blockchain is thought to be so impactful that it is considered to be the next big thing after the internet.

Till date, the most prominent use of blockchain is for payments of bitcoins. Though bitcoin has the advantage of being transferred instantaneously, it has not made significant progress as a well-accepted currency. Bitcoin has neither caught up as a preferred means of transaction, nor is it expected to replace the existing currency system.

Though banks, customers and regulators have largely stayed away from bitcoin, the underlying technology used to conduct bitcoin transactions - blockchain, and the concept of distributed ledger has started getting industries thinking on how this new technology can be used to their advantage. Though its wide stream application is still to be seen, banks have started realizing the power of blockchain and are contemplating investing in the technology. Banks are now betting on blockchain as a reliable alternative to processes that are intermediary-dependent and effort intensive.

What is Blockchain?

Blockchain is an electronic distributed ledger for all bitcoin transactions where completed transactions are added in a chain-line linear chronological order as "blocks" of records. Transaction information that is once verified and recorded as a block cannot be removed or modified, thus making them secure and tamper-proof. Each block is linked using a hash address of the previous block, thus sequentially recording the transactions. This ledger is shared by all members (nodes) participating in the ledger network and uses cryptography to protect against any tampering of information.

These cryptocurrency networks can be 'permissionless', meaning, anyone can perform a transaction or a validation anonymously. There are several risks in this type of blockchain due to the presence of users who are no completely trustworthy.

The 'permissioned' blockchain networks, on the other hand, are those which permit only approved persons or parties to post transactions or validate the network.  Many risks that are present in the permissionless blockchain are done away with, in these permissioned blockchain. Hence this is the preferred choice of banks and regulators alike.

Which industries can be benefitted by Blockchain?

Blockchain is a database and contradictory to popular belief, it is not a finance specific technology. It can be used in any industry where there is a need to store data.

This technology has particularly caught the fancy of financial institutions, but now more and more non-financial sectors like retail, real estate, insurance are taking the baton. To generalize, it can be said that blockchain technology can be used in any industry that has a lot of record-keeping and repetitive documentation.

In banking particularly, though blockchain technology can be used in any banking process, it is thought to have a greater impact on the back-end clearing and settlement process than the front-end processes.

The potential of blockchain need not be limited only to one industry. Cross industry implementation of blockchain is another way this disruptive technology can be explored.

Regulatory aspects

Though it has the disadvantage of being slightly risky due to the absence of regulatory bodies, there are currently no regulations in place for the use of blockchain technology. European Securities Market Authority (ESMA) is currently taking a 'wait and see' regulatory approach to blockchain, while Financial Institutions Regulatory Authority (FINRA) has warned that mismanagement in blockchain could increase the vulnerability in markets. Whether multiple regulatory bodies or a single entity would regulate and govern the blockchain use, is yet to be decided. The presence of a regulatory body would definitely help as they would then mandate standards for interoperability as well as sharing of costs/revenues.

Banks must not overlook the regulatory uncertainty that is still prevailing in this area and hence must exercise caution when experimenting in blockchain.

Conclusion

Blockchain is not just a new technology, it is a very exciting technology to begin with. It can improve as well as disrupt several industries. It is also something that banks and other industries still have to explore deeper. Industries need to partner and collaborate with technology providers/start-ups and regulators and ideate on how this new disruptive technology can be leveraged to its true potential. Demonstrating this successfully can not only help organizations cut on infrastructure and processing costs, but also improve customer experience.

However, organizations should not rush to adopt Blockchain, without establishing a solid business case and benefits. They must carefully strategize and identify the area of operation that would most benefit from this technology. Remember, blockchain is not the answer to everything!

June 28, 2017

Artificial Intelligence and Regtech: The changing face of compliance in banking

Regtech is often referred to as the "little brother of Fintech". Little wonder then that much like Fintech, Regtech is rife with action. While Fintech was born out of the need to redefine customer experience that traditional financial services offered, Regtech rose after the financial crisis of 2008, when several banks faced heavy fines.

Financial institutions of today are grappling with a complex regulation and compliance landscape. They need to store and provide data to regulators in ways that are faster, reliable and cost efficient. And technology is increasingly becoming the default answer to this complexity.

Artificial intelligence in banking, though it has been around for some time, is finding several applications in the risk and compliance space. This is mostly because AI cannot only sift through massive amounts of data in seconds, but it can also establish connections in totally unrelated or unstructured data sets. Case studies of AI being deployed to smoothen the compliance process are emerging rapidly. Deutsche Bank recently deployed AI to sift through volumes of recordings - both voice and video - to ensure compliance. The technology can automatically search for terms that auditors monitor, saving the bank hours of manual intervention.

One of the biggest regulation challenges that AI is helping financial institutions deal with is completing regulators' "stress test". This involves modelling, scenario analysis and forecasting and is both a time and cost intensive process. Citigroup recently deployed an AI system from Ayasdi, a Stanford University spinout, to help it go through the US Federal Reserve's stress test. This system uses topology to recognize data patterns and identify complex relationships.

Another important area that AI is addressing for financial institutions is managing customer identity. AI based systems are helping banks onboard customers faster, and bringing in more accuracy in Know-your-Customer (KYC) processes. By automating these processes and achieving orchestration between siloed processes like client on-boarding, legal, technology and compliance, financial organizations can achieve a reduction of 60-80% in their client on-boarding time.

Risk-data aggregation is the other compliance area where banks are deploying AI. Since this involves real-time analysis of huge amounts of data, AI seems like a perfect solution. Machine learning algorithms can help financial institutions identify repayment patterns and predict the chances of default. A good example here is the Aidyia hedge fund, which uses AI to drive all its trades, without any human intervention.

Apart from helping financial institutions improve several compliance processes, AI is also acting as a bridge between Regtech startups, regulatory bodies and financial organizations. The entire ecosystem is coming together to collaborate on not only making compliance effective, but also to drive data driven decisions. Since compliance processes require collection and aggregation of a huge amount of data, Regtech startups are going a step ahead and helping banks derive value from this data.

May 31, 2017

5 ways in which artificial intelligence is redefining banking

Artificial Intelligence (AI) has become an oft heard buzzword in the financial services industry. Be it improving customer interactions, analyzing millions of data points in seconds or detecting fraud. As I attended Infosys Confluence 2017 in San Francisco last week, and interacted with several banking and financial services leaders, one thing became stark clear- AI in the banking industry is no longer about pilot projects or enthusiastic experiments. It has established its value as a technology that can significantly improve the banking experience in the near future.

As AI adoption gathers pace, several aspects of banking are set for a makeover. Here are the 5 key areas that I think will be most significantly impacted with the rise of AI:

The banker bots: The bots are everywhere. And they are redefining the way banks are delivering customer experience. Be it Swedebank's Nina or Mizuho Bank's Pepper, virtual assistants have made their way right up to the customers in the banking ecosystem. As more and more banks adopt chat-bots, the technology behind them- Artificial Intelligence- is set to learn, evolve and become more agile and efficient. As a result, we are likely to see even more bots becoming bankers.

Catching the fraudsters: PayPal, which processed $235 billion in payments last year from over 4 billion transactions by more than 170 million customers, uses a deep-learning system based on AI to detect fraud. Not only does the system flag unusual transactions, it also profiles these frauds as a "feature," or a rule that can be applied in real-time to stop purchases that fit this profile. This has helped keep PayPal's fraud rate remarkably low, at 0.32 percent of revenue--a figure far better than the average of 1.32 percent that merchants see, according to a study by LexisNexis. As AI adoption gains traction, more and more banks are likely to utilize AI technologies for detecting and combating fraud.

Smartening the back-office: While the customer facing side of AI technologies is well elucidated, we often miss out on the impact that AI is having in the back-office operations of banks. AI is removing thousands of man-hours from banks' sheets by reviewing loan agreements, identifying repayment patterns and bringing in Robotic Process Automation (RPA) to populate data entry and increase processing speed, especially for structured data. This part, according to us, can be real game changer for banks in saving costs and increasing efficiencies in the near future.

Making data-based, real-time recommendations: Banks have often struggled to make relevant recommendations to their customers about their products and services. The model that traditional banks have been following is creating standard recommendations for a set of customers and flashing them at various point-of-contacts, without any real targeting metrics. AI engines are set to change all that. These self-learning systems are cultivating user data based on their behavioral patterns, banking history and in some cases even their public profiles to make suitable recommendations to users. In recent times, banks have been utilizing these recommendation engines as a key tool to upsell and drive incremental revenue.

Bringing Fintech innovations to customers: A lot of AI innovation is happening in the Fintech space. Companies like Anki and X.ai are reducing human intervention in customer interactions. These innovations are propelling banks to integrate specialist third party services from niche start-ups in a very flexible way and bring these services to their customers. New tools are facilitating integration and cognitive agents are making it faster to train and activate a customer facing agent to sell these services to all clients. Thus, collaboration, rather than competition, is booming between banks and Fintechs, thanks to technologies like AI. While banks have the edge of an already established customer base, and their ability to scale offerings quickly, Fintechs are bringing in the innovation factor to the banking party!

As AI adoption accelerates, we see more advanced use cases emerging for banks, such as identifying opportunities from data and actively suggesting intelligent, dynamic policy changes. The speed and scalability of cognitive technologies will result in a slew of growth opportunities for banks which incorporate these approaches into their strategy.

May 19, 2017

Artificial Intelligence in Capital Markets: a tale of opportunities and Fintech collaborations

According to a recently released IDC spending guide, worldwide Cognitive Systems and Artificial Intelligence revenues are forecast to surge past USD 47Billion in 2020. According to another research firm, Opimas Research, in 2017, financial firms alone will spend more than USD 1.5 billion on artificial intelligence (AI) related technologies and by 2021, USD 2.8 billion, representing an increase of a whopping 75%.

Capital Markets, like every other space, is seeing a surge in technological solutions that are coming of age and delivering increased performance- especially in areas of advanced analytics, real time trade-processing platforms and improved regulatory compliance. These technological solutions in a way, have come as a panacea for Capital Market firms, which are grappling with increasing compliance costs and shrinking bottom lines.

Thanks to these macroeconomic factors, Capital Market firms are increasingly looking towards advanced technology solutions like AI to increase employee efficiency and aid faster decision making.

As a technology, AI already has established use cases in areas of client relationship management, trade execution, reconciliations, transaction reporting, tax operations, and several other areas.

We see initial implementations like robotic process automation for reduced manual errors and improving process speeds by automation of repeatable IT tasks. Even as the Capital Market firms explore more advanced use cases for AI, a few areas that we are seeing pilot adoptions include speech recognition, machine learning platforms and virtual agents.

However, the siloed legacy infrastructure that most of the capital market institutions have, coupled with lack of a cohesive, top-driven automation strategy are acting as impediments in the way of effective AI implementation. In such a scenario, many Capital Market firms are taking the route of small, targeted phases of adoption, which can scale in sync with their IT infrastructure.

Another aspect that we find interesting, is the alternate route that these organizations are looking to leverage for bringing in AI and other automation technologies into their ecosystem -- partnerships with Fintech players. Capital Market firms, much like banks, are partnering with Fintech players for things like AI driven post-trade processing platforms and advanced analytics. The most prominent model of these engagements as of now is via accelerators and labs, and the next phase can bring in investments and acquisitions.

As Capital Markets gradually move up the AI value chain, we can expect more Fintech collaborations, advanced use cases for areas like fraud detection and prevention and anti-money laundering activities.

Keep watching this space for the latest in banking technology and trends!