EdgeVerve Brings Automation Built For Everyone With AssistEdge

Automation And AI Are Key Drivers Of Digital Transformation

Digital transformation isn’t breaking news, but now that it is progressing at an unprecedented rate, enterprises should make it a part of their strategy to on-board this wave. To overcome this breakneck pace of digital change, companies across sectors have begun adopting digital-led business models and automating processes to drive efficiency by reducing costs and improving operations and customer experience.
The past few years have witnessed intensifying interest in Robotic Process Automation (RPA), and automation remained the top strategic agenda for organizations seeking to streamline mundane and monotonous processes and adopt a more customer-centric approach. However, today, digital transformation is a journey and not a destination and the current paradigm goes beyond replication of existing tasks/processes. Transforming business processes to unlock significant business value through a potent combination of automation with Artificial Intelligence (AI) is the way ahead.

EdgeVerve -Automation Built For Catalyzing AI

Companies in today’s hyper-competitive landscape are looking to go beyond automating tasks, towards creating new and ongoing value for the organization. Encompassing technologies like machine vision, natural language processing (NLP) and machine learning (ML), AI can help create an intelligent automation system capable of delivering far more potent capabilities for fraud prevention, brand management, customer service, software testing and development, and human resource management.
Intelligent Automation is the key differentiator for businesses to navigate their digital transformation journey. These technologies are just beginning to emerge and their business benefits are much broader. For instance, classifying unstructured data using advanced capabilities such as Natural Language Processing and Optical Character Recognition is just one example. Using AI capabilities to identify processes for automation or auto-scaling existing bots or conducting sentiment analysis to drive decision making, are some more business benefits.
Every business, be it retail, telecom, automotive or any department, HR, finance, operations should be future-ready with a focus on innovation. EdgeVerve offers intelligent automation built for everyone with its solid AssistEdge, a RPA solution powered by the purposeful AI platform Infosys Nia™. It is built for enterprises who are ready to scale and reap the benefits of digital transformation led by automation and AI.
Currently, AssistEdge is already helping drive efficiency and deliver concrete business value for enterprises by automating 10,000+ processes across 50+ countries, saving $2billion for customers across industries.

Building Future-ready Enterprises With AssistEdge RPA-AI Convergence

The convergence of RPA-AI delivered by AssistEdge enables an organization span effectively through the complete automation continuum, from deterministic through predictive, to cognitive stages. It leads an enterprise to become an insights driven business by utilising data from automation of processes. Driven by ML and NLP capabilities of AI, it sets the foundation to derive relevant business insights.
For instance, RPA addresses specific challenges in the financial world, helping banks minimize human intervention in executing rules-based tasks and enhancing compliance. However, RPA augmented by AI goes far beyond, by emulating complex processes that need some amount of judgement or decision-making capability. It can improve the detection of financial fraud by learning from the extensive customer data driven insights. Similarly, automation for retail is capable of addressing more complex challenges by helping retailers not only react to consumer needs but also proactively predict and prepare for them. The telecom industry could create new personalized customer experiences, improved customer response time and offer customers the right plan at the right time. Similarly, intelligent automation for data-driven healthcare can fundamentally transform the patient experience by enabling proactive care, and enhance the drug development pipeline by accelerating the process of drug discovery, ultimately unlocking massive revenues for businesses in the sector.
With end-to-end solutions combining AssistEdge and Infosys Nia, EdgeVerve will continue to lead the way as well as guide enterprises in their digital transformation journey with intelligent automation built for everyone.

Artificial Intelligence – Man Vs Machine

Human civilization has come a long way since the times of using Stone Age and Iron Age implements for hunting, agriculture, mobility and in general, for addressing all aspects of human existence. The basic urge to improve the quality of life by using tools is perhaps hardwired into the human brain as evidenced by many inventions across all the cultures of the world including primitive societies of today. With the advent of industrial revolution in England in the 17th century, machines came to be invented and the power of coal, and later electricity and other sources of energy came to be harnessed for purposes which were until then undertaken by manual or animal labour or were even impossible to accomplish.
One basic characteristic of all human inventions which continues unabated till date has been that though they helped reduce the drudgery of humankind, they were designed to merely obey human commands and often perform repetitive jobs with immense power, precision, unerring accuracy and efficiencies bordering on the unfathomable. In other words, most machines were “dumb” and possessed no “native” intelligence that would enable them to learn, memorize and execute tasks of ambiguity like humans do.
With the exploding scientific, medical knowledge on human brain anatomy , neural networks, and the computational power being made available these days, it is now becoming possible to design machines that think like humans, are self-learning and can possess all the traits that a human brain encompasses. But the more important thing is that these machines are embedded with machine learning statistical algorithms that enable the machines to improvise on their tasks by discovering new and better ways of execution which humans may take more time and intuition to evolve. In the process they can almost completely eliminate the common errors prevalent in human transactions and improve the quality of job execution manifold. Taken to its conclusion, a fully human like machine in thought, look and action can be a troubling philosophical proposition for what this means for humanity’s own perception of itself, but it opens up exciting possibilities in many fields which hitherto required human intervention at different levels and were hence prone to errors.
For example, humanoid robots (machines which resemble humans in look and behaviour) can be designed to undertake surgeries which need be done with precision that are not available to human hands and eyes. Similarly humanoid caretakers can assist with geriatric care in a rapidly aging population and even comfort the lonely and depressed by offering much more humane care and succour. Humanoid pilots can possibly fly airplanes without the rigor of training and wont sleep off on long duration flights and the future armed forces may likely be manned by humanoid “beings” who will carry out war operations on behalf of the country owning them. Mundane operations like banking are no exception and will be completely owned, operated 24*7*365 with little human involvement.
The difference between humans and humanoid “creatures” will blur with the evolution of artificial intelligence and one will never know if the humanoid capabilities may even outstrip the human brain’s capabilities, and one wonders if they will eventually take over the earth from human beings. Overall, the future of artificial intelligence looks both exciting and foreboding. Humankind seems to have turned full circle with machines based on artificial intelligence because they imitate human beings themselves. Only time will tell the course and impact of this technological innovation on the future evolution and existence of humankind.

Blockchain and possible impact on currency handling

Blockchain as the name implies refers to forming a chain of blocks with each block consisting of multiple records. These records could be bank transactions, population details, weather data and so on. The key feature of Blockchain is that its data is cryptographically encoded using mathematical algorithms by a process called “Hashing” to generate a unique hash number for each block. Further to this, each block is linked to the previous block by using the previous block’s hash number to generate the present block’s hash number. This means that if the data in any block is tampered with, it immediately reveals itself as the hash number linkage to all subsequent blocks is be lost.
Each block preserves the identities of transacting persons by way of another cryptographic scheme called public-private key. In this scheme, a key generating program at the transacting person’s end generates two keys, a private key and a public key and then encrypts (or digitally signs) the transaction with the private key. The public key is broadcast to everyone on the network who can verify the authenticity of the message details using the public key. The public key constitutes the identity of the transacting person.
Transactions which are verified for all details are clubbed together to form a block and are added to the chain by special nodes on the network called miners. Miners, which are powerful parallelized computers, compute the hash of the block by a computationally intensive process called mining and once hash is computed per specifications, the block is added to the chain and the chain keeps growing. There is no administrator or central node to own the blockchain as such and hence the cost of ownership is next to nil. Also it is impossible for hackers to break into a blockchain based ledger as the hackers generally target vulnerabilities on a centralized server.
Blockchain leads to the concept of digital currency or crypto currency of which Bitcoin is a good example. There are other lesser known currencies including Ethereum, Ripple Litecoin etc. and more are in the offing. The more important question is how can crypto currencies which have no intrinsic value as in the case of gold or real estate be justified or linked to government mandated fiat currencies? Bitcoin for example rewards the miners with bitcoins for adding blocks to the blockchain and also puts an upper limit on the amount of bitcoin that can ever be produced.
The value of a nation’s currency is determined by its trade volumes, natural resources and overall state of its economy and its people. Ever since human beings gave up the barter system and adopted the currency system, different country currencies have freely fluctuated against each other in market economies run without true government interventions. But since we live in a digital age it is only a matter of time before even currencies go digital, and definitely with sanction from governments and central banks, the world over. The concept of digital currency is partially facilitated by using payment instruments and electronic payments controlled by regulators. The introduction of digital currency might remove the fake currency grey market and a parallel economy which is running. But it remains to be seen how these crypto currencies will be linked to existing currencies especially in terms of value.
In the event of all transactions completely going digital, it is not infeasible to think that digital currencies will be the order of the day sometime in the future. Also since a lot of world’s wealth today still exists in books and bank accounts without being realized in hard currency, moving into crypto or digital currency regime won’t be farfetched. But when it comes to determining their value and hence the amount in circulation, central banks and governments will have a hard time. The risk factors and other regulatory aspects need to be worked out before arriving at a conclusion on the acceptance of crypto currencies world-wide.

Acing the API Game? Not Really, Banks Need to Pace it up Instead

After years of gradual growth, the financial services industry saw an exponential rise in the number of FinTechs in 2014 and 2015. Banks were not only compelled to sit up and take notice but in a bid to protect their turf, they developed a combative approach and started competing with these agile upstarts. With time, the approach shifted from combative to collaborative as the concept of symbiotic interplay between FinTechs and banks gained ground. According to a research by Efma, about 91.3% of banks and 75.3% of FinTechs expect to partner with each other in the near future. Collaboration has become the norm and APIs are the key enablers making it happen. And it’s not only collaboration between banks and Fintechs, but collaboration with diverse ecosystems consisting of third party digital natives and individual developers. There is a clear shift from the traditional pipeline to a platform business model, with banks aiming to become distributors or aggregators of products. Some early mover banks are already benefitting from the use of APIs and believe that they have the first mover advantage to become dominant players in the future. What’s more, forward looking regulations such as PSD2 and Open Banking that require banks to open access of customer data to third parties, further put APIs at the center of next generation banking.
There is little dispute that the future of banking is open, and that APIs are the cornerstone of this future. To understand API models in banking, Infosys Finacle studied the offerings of 6 leading banks that have already launched API stores, and found out that these APIs were published in a maximum of 11 categories with most banks restricting them to about 8 categories. These banks had somewhere between 5 to 50 APIs, clearly not a significant number for the industry to claim to have a stable model that earns revenue. Moreover, most of these APIs are running with dummy data in sandbox environment. Banks need a definite API strategy to progress on their platform vision. The fundamental questions they need to ask themselves are:

  • Who?

    For every API they expose, banks must be clear about who it is intended for. A bank may expose certain APIs only to a select list of partners, enabling them to create applications that are then deployed within the bank. Or a bank may release its APIs to contracted partners to build apps on top of these APIs. Third, to deliver banking-as-a-service, a bank may choose to expose 100 percent owned banking APIs publicly, so that any third party developer or Fintech company can access it to build its own applications.

  • What is the coverage of APIs vis-à-vis the business vision?

    Banks must expose APIs in line with their vision. For e.g. transaction related APIs differ in abstraction from non-financial APIs. Thus banks must keep a track of the number and the kind of APIs they expose.

  • What is the granularity of the APIs being exposed?

    It is important to decide the level of granularity. For instance, will a single API cater to all customer profile updates, or will there be separate ones for address and telephone number? While a monolithic API is easier to maintain and manage than multiple small APIs, it exposes the bank to greater risk and gives users less flexibility.

  • What is the data being shared?

    Most banks currently have APIs running in a sandbox with dummy data. Banks need to have the necessary governance mechanism in place to take their APIs live with production data, which is where the real value lies, and any conversation around monetization of APIs can begin.
    Emerging technologies such as AI, blockchain, etc. are potentially expanding the definition of banking. Banks aspire to embrace that definition of banking to integrate banking in the lives of their customers. But they seem to be a long way away from realizing that vision. APIs form the core of every interaction in the digital world, and banks need to move fast to be able to become a distributor of products or to bring external innovations directly to the banking customer.
    Why, 5 of the 9 banks could not meet the deadline for PSD2 in the UK. Is that some indication of the ground work needed to realize the digital banking dream?
    You might find our paper on ‘Platform Business Model for Banking ‘ interesting, you can access it here.

References:

  • Infosys Finacle Banking as a Platform Point of View

  • World Retail Banking Report – EFMA and Capgemini

Why Banks need an ‘AI-First’ strategy

In June 2007, Apple launched the iconic iPhone, decisively pushing the world into a ‘mobile-first’ era. What followed was a mad dash among all consumer industries, including banks, to get into the precious real estate which had every user hooked for hours.
10 years later, a similar halo of excitement surrounds the oft heard and frequently abused term, ‘Artificial Intelligence’. That this technology, along with its partners ‘Machine Learning’ and ‘Automation’, would unleash a bigger revolution than smartphones is almost a given. The possibilities are endless; but what will it mean for organizations to use AI and transform their offerings? Specifically, what would ‘AI-first’ banking look like?
There are banks like Capital One who have gone ‘Voice-First’ by integrating with Amazon Echo. All you need to transfer money to your sister is wake up Alexa and tell her to do exactly that! Ergo, your Capital One account will be debited and money wired to your sister.
WeChat, a popular messaging platform in China, provides instant micro loans to its customers without any human involvement. The insurance arm of Alibaba group uses neural networks trained on images of cars involved in accidents to calculate settlement amounts. Anyone involved in a car crash only has to take a few photos of the damage and upload on the mobile app. The AI driven backend system has the ability to settle 2 claims per second. There are Digital Only Banks, with zero branches, in most geographies of the world that perform automated onboarding and KYC through artificially intelligent audio, video and image recognition.
Banks have started experimenting with certain other use-cases too. Chatbots and Robo Advisors are two of the currently leading AI innovations. Just last year, there were more than 30,000 payment chatbots integrated with Facebook! Innovative banks now allow users to transfer money, check balances, answer queries and make loan applications via Facebook and Twitter.
As of August 2017, Robo Advisors had more than 400 billion US dollars of assets under management. All decisions regarding buying, selling and the percentage of capital allocations for these mutual funds were made by algorithm driven robots. They are not doing badly either; by 2020 AUM controlled by them is expected to reach 8 trillion US dollars!
Some futuristic AI driven use-cases include your fridge using its own wallet to order food, your digital assistant applying for a micro loan, a robot completing a factory visit to assess a plant and machinery loan and self-driven ATM vans which you can order to your doorstep for cash.
For the large corporate enterprise the shift in the banking experience has begun with automated loan applications and derivatives structured and monitored by algorithms. The reduction in time and paper work would be the biggest gain at the enterprise level. It would not be surprising if in the future a bank just employed a robot to cater to all the needs of its corporate customer, right from providing interest rate quotes, transferring loan funds, providing on demand financing, doing factory visits to even leading negotiations!
While ‘mobile-first’ was a revolution led by a single device, ‘AI-first’ in banking would be a more holistic experience driven by home speakers, perky digital assistants, voice & touch apps and IoT wallets for the consumer. Even with the movement to voice, the touch based smartphone will exist as a supplementary device. It is a certainty that back end banking infrastructure would move to cloud and be managed by third party vendors.
Because with AI, banks as we know them will disappear. In a generation’s time, there is a possibility that branches, which are huge overheads and a drain on the balance sheet, may completely disappear or be turned into museums. Banks will be invisible and will exist as a service embedded in the products and services we consume.
The question is: Are today’s banks ready for the transformation? If one were to scan the landscape, the biggest investments in AI are being made by technology giants like Google, Facebook, Amazon and Intel. The leadership in AI comes at a price; billions are being spent right now in making acquisitions and developing capabilities; but the payoff cuts across sectors. Once AI capabilities are developed, they can be deployed across various sectors, banking just being one of them. An enterprise like Facebook or Google can offer banking services to rival the best banks at much lower costs and with exponentially higher processing speed.
The technology giants of China like Baidu, Tencent and Alibaba have understood this and already offer a host of financial services automated through the use of AI in the back end.
At this stage, deep-learning-neural-network drive the interest in AI. This is primarily due to limited expertise and massive data requirements. But as the interest in AI grows, the technology would be more widely available. ‘AI-first’ would become ‘AI-Only’ then, as automation would do a far better job than humans in most of the roles.
Traditional banking organizations are facing their ‘Kodak moment’. They need to develop and invest in an ‘AI-first’ strategy right away. Technology giants of Silicon Valley and China already have a huge lead. The time to innovate has shrunk rapidly in the past few years. By the time the world moves to ‘AI-Only’, many of today’s financial behemoths would either have transformed or become obsolete. The time to make the choice is now.