The Infosys Labs research blog tracks trends in technology with a focus on applied research in Information and Communication Technology (ICT)

May 29, 2019

Multi-cloud is the way for cloud adoption

The model of Multi-cloud is mix and matching the best-of-class application, solutions and services from more than one cloud infrastructure providers for creating the most suitable IT landscape for a business.  Deployment can make use of public, private clouds, or some combination of the two. Another aim of multi-cloud deployments is to offer redundancy in case of infrastructure failures and avoid vendor lock-in. In hybrid cloud model, organizations use a combination of public cloud, private cloud and on-premises services, wherein in multi-cloud model employs multiple cloud services from more than one provider. These approaches can coexist, example of an organization that uses a private cloud solution, an on-premises server and different public cloud solutions would have an IT strategy that is both multi-cloud and hybrid cloud.

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May 23, 2019

Cloud Migration in Banking Industry

Cloud computing is the on-demand delivery of computing resources like processing power, data storage, applications, and other IT infrastructure through cloud platform via internet having pay-per-usage pricing model. Broadly cloud services can be classified under three categories service models (SaaS, PaaS, IaaS), deployment models (private cloud, public cloud, hybrid cloud, multi cloud), and cloud stack (facility, network, computer and storage, hypervisor, virtual machine, solution stack, applications, API/GUI). The major areas in cloud services across the industries are migration, security and privacy, regulatory adherence, cloud applications, cloud advisory, and testing.  With cloud migration gaining traction in banking industry in a major way.

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March 25, 2019

Continuous Authentication

The world is rapidly changing; smart phones, laptops, and wearables have altered the way we live. Today people frequently switch between and share devices with each other. Over the years, organizations and regulators have mandated the authentication of users in an attempt to prevent fraud and protect personally identifiable information.

Increased focus on user engagement has forced companies to avoid excessive authentication related tasks in order to enhance customer experience. However, by adopting this strategy they risk compromising customer data and hacking. Conversely, fraud avoidance is also a primary concern for organizations and they enforce extra barriers such as two-factor authentication and short login timeouts which increase account security, but it comes at the cost of user experience.

Continuous authentication has emerged as a promising option for companies trying to provide secure services to clients in a digitally vulnerable world. Continuous Authentication is a dynamic, risk based authentication that changes the procedure of authentication such as event based authentication used today to a process. Instead of users logging in or out, the application continuously authenticates the user through behavioral biometrics.

Continuous authentication uses behavioral biometrics to enable organizations to move away from one-time authentication, thereby increasing security and improving user experience. Behavioral biometrics uses machine learning to continuously monitor the user's behavior based on interactions with apps and websites. Malware and frauds with login credentials are easily identifiable with the application of continuous authentication. Yet, behavioral biometrics' s biggest advantage is that the technology is capable of working with existing infrastructure without requiring any modification.

Another key advantage is the ability to assign user action constraints based on tolerable risk or context. These constraints can be based on factors such as location, presence of other people or the time of day. Infact, behavioral biometrics has been approved by the PSD2 as a valid authentication option.

Startups like BioCatch, authenticate users continuously when their online and protect them from cyber threats. Some examples are Trojans, account takeover and other malware. The company is already providing real-time fraud prevention for 2 billion sessions a month.

Continuous authentication is the first true alternative to pesky passwords and an effective method to prevent fraudulent transaction which amount to around $130 for mobile transactions and about $115 for tablets on a normal day. The technology stands to secure the way interact with the digital world while streamlining the entire experience.

References:

https://blog.securedtouch.com/strengthening-mobile-payments-with-continuous-authentication

https://www.csoonline.com/article/3179107/continuous-authentication-why-it-s-getting-attention-and-what-you-need-to-know.html

https://www.okta.com/security-blog/2018/03/what-is-continuous-authentication/

https://www.networkworld.com/article/3121240/continuous-authentication-the-future-of-identity-and-access-management-iam.html

Wearables in Banking

Wearables are electronic devices incorporated into items that can be comfortably worn on a body or embedded into accessories. According to IDC, the wearable tech market will see shipments almost double to 240.1 million by 2021. Wearable adoption has been driven by higher acceptance and adoption by newer generation due to its positioning as a standalone device and the evolution of operating systems which are more user friendly.

Wearables enable bankers to develop customer centric solutions and offer hyper-personalized offerings to clients. The technology enables organizations to collect real-time behavioral data which can be leveraged to provide personalized and real time offers similar to google and amazon. To Illustrate, consider wearable fitness devices which monitor the wearers movements, location and activity level to advise users. Similarly, banks can leverage the technology to become their 24/7 personal banker.

Another advantage that the technology provides is the autonomous verification of customer identity. Wearables enable users to authenticate transactions without having to remember password by using biometric technology. The feature would enable banks to streamline services and customers to initiate payments or withdraw money from ATM with the wave of a hand. For instance, The Australian and New Zealand Banking Group (ANZ) is now accepting cash withdrawals via smartwatches at 2,400 ATMs across Australia. Similarly, Gemalto's MiniTags and MicroTags have already been certified by Visa, MasterCard and Amex. And can be used at all locations where these companies accept contactless payments.

Advancements in biometric technology have helped financial institutions combat rising concerns of fraud and identity theft. An example is the Apple Watch which utilizes plethysmography to identify if a body part (in this case, the wrist veins) has increased or decreased in size to identify the user. Banks will also need to leverage architectural design and prevention analytics to protect customers against threats such as Bluetooth theft, signal interceptor issues, and virus attacks.

Multiple financial organizations have already taken the lead in the adoption of wearables. Barclays has partnered with brands like Topshop, Garmin and Mondaine to design devices compatible with its bPay chip in order to enable customers to make payments with a range of wearable devices. Similarly, ABN AMRO bank is testing smart rings, watches, and bracelets as a new NFC payment method. Companies like US Bank, Wells Fargo and Citibank have also embraced wearables by provide balance tracking and notification to customers through smartwatches.

In conclusion, wearables are going to revolutionize the way organizations interact with their customers and prospective clients. Organizations can only meet rising consumer expectations by using the technology to interact with their customers outside the branch. Wearables are poised to become an integral part of our everyday life and provide a doorway for companies to provide cater to our needs.

References:

https://www.happiestminds.com/Insights/wearable-technology/

https://www.i-scoop.eu/wearables-market-outlook-2020-drivers-new-markets/

https://www.iotforall.com/future-wearable-technology/

https://www.bankingtech.com/2018/01/how-wearables-will-change-everything/

https://www.bankingtech.com/2018/01/how-wearables-will-change-everything/

https://www2.deloitte.com/content/dam/Deloitte/us/Documents/process-and-operations/us-cons-wearables-in-banking.pdf

https://www.ngdata.com/what-is-personalized-marketing/

https://www.nfcworld.com/2018/09/25/358126/anz-lets-customers-use-smartphones-and-smartwatches-to-withdraw-cash-from-atms-across-australia/

https://www2.deloitte.com/content/dam/Deloitte/us/Documents/process-and-operations/us-cons-wearables-in-banking.pdf

Voice Analytics in Financial Services

Voice analytics is the use of a voice recognition tools to analyze a spoken conversation. The technology has the potential to increase the performance of client facing operations and the value of every customer interaction. According to Grand View Research, the voice analytics market to reach $1.64 billion by 2025 and Opus Research expects companies 57% of companies to increase sales and collections.

Customer satisfaction is one of the key drivers of voice analytics, the technology offers the capability to understand the emotions hidden behind the customer's words and analyses whether an expression is neutral, negative or positive, and to what extent. The solution offers a trustworthy alternative for post-call surveys and helps identify where agents fall short and excel to help analyze and tweak agent performance in real time. An example is ING Bank, the bank has leveraged voice analytics to uncover issues that require improvements in customer experience, agent performance, sales potential, and service quality. In addition to enhanced customer experience, the bank the solution reduced telecom costs thanks to shorter calls.

Increasing regulations is also a critical driver of voice analytics. Voice analytics based compliance is a trending topic which helps banks by offering risk-based quality monitoring. The solution leverages analytics to identify phrases that indicate mis-selling or other compliance failings. The banks can then isolate, follow up and remedy the issue, within hours. Another aspect of compliance where the technology has great potential is authentication, voice analytics helps banks to provide faster access to services by verifying customer identity in an ambient and seamless process. For example, Barclays reduced the authentication process by an average 20 seconds using voice recognition. It minimized identification and authentication issues which resulted in increased customer satisfaction.

In conclusion, voice analytics is set to radically change the way we interact with customers. While offering enhanced insights into their customer's satisfaction and interests, the technology also helps banks adhere to regulatory protocol. Hence, the adoption by banking giants such as Ally bank, ABN AMRO, US Bank, TD Bank, Santander, Citibank and Bank of America.

 

References:

https://callminer.com/blog/what-is-voice-analytics-definition-tips-best-practices-and-challenges-of-voice-analytics/

https://www.grandviewresearch.com/press-release/global-speech-analytics-market

https://www.uniphore.com/landingpage/executive-survey-on-speech-analytics-2017

https://www.businesswire.com/news/home/20180223005685/en/2.17-Billion-Global-Speech-Analytics-Market-2018-2022

https://www.uniphore.com/blog/2018/04/top-5-ways-speech-analytics-is-helping-the-banks-offer-better-customer-service

https://www.spitch.ch/blog/the-competitive-edge-speech-analytics-in-banking-customer-services/

https://www.uniphore.com/blog/2018/04/top-5-ways-speech-analytics-is-helping-the-banks-offer-better-customer-service

https://www.bai.org/banking-strategies/article-detail/speaking-up-looking-up-why-banks-need-real-time-speech-analytics-for-compliant-customer-interactions

https://www.businesssystemsuk.co.uk/blog/2017/05/03/using-speech-analytics-retail-banking/

https://www.businesssystemsuk.co.uk/blog/2017/05/03/using-speech-analytics-retail-banking/

https://www.theglobaltreasurer.com/2016/09/29/applying-speech-analytics-to-financial-services/

Computer Vision in Banking

Computer vision is an AI application that provides computers the ability to visually understand the world. The technology is capable of automatic extraction and analysis of relevant information from images. According the Grand View Research, the global machine vision market size is expected to reach USD 18.24 billion by 2025.

KYC is an important usecase for computer vision. The technology enables banks to shift their focus from duplicate databases and use biometrics for identification of prospective clients. The technology streamlines the KYC process by allowing prospective customers to open accounts over their phones. The European bank BBVA is attracting customers by allowing prospective customers to open an account with a selfie or video call. The solution helps banks increase customer convenience and move further in the customer centricity journey.

The technology also helps organizations analyse customer behavior in real time through emotion recognition by analyzing micro-expressions, pupil dilation and eye moments. Banks can capitalize of the information by offering personalized products and modifying process to make it more convenient for clients. Similarly, computer vision can track intent of individuals near cash points and detect threats in real time, thereby providing a safe environment for their customers.

Back office operations would also benefit from applications of computer vision. The technology would streamline back office operation by reducing the paperwork through automated data extraction. The solution would help banks save time and cost involved in processing of documents by humans while increasing accuracy and efficiency.

Computer vision also helps traders by providing granular data for making trade decisions. Investors and economists can track data such as automotive activity in a retailers parking lots and shipping container movement in ports to identify trends in the economy.

The rise of AI has opened an exciting opportunity for the financial sector which has been burdened by the mounting paperwork and regulations. Computer vision is poised to revolutionize everything from back office operation to how investors evaluate a company, ubiquitously.

References:

https://hayo.io/computer-vision/

https://hackernoon.com/computervision-ee192f917646

https://www.grandviewresearch.com/press-release/global-machine-vision-market

https://www.finextra.com/blogposting/16210/is-the-financial-sector-ready-for-innovation-with-computer-vision

https://emerj.com/ai-sector-overviews/machine-vision-in-finance-current-applications-and-trends/

https://hackernoon.com/computervision-ee192f917646

https://www.globalbankingandfinance.com/video-ai-the-future-of-finance/

https://indatalabs.com/blog/data-science/applications-computer-vision-across-industries

March 20, 2019

Future of Personalized Assistant

The adoption and usage of smart assistants is gaining popularity with more and more people using google, apple, or amazon assistants for navigation, searching, scheduling, doing mails etc. With advent of new age of communication and application based digital economy the usage smart assistants will be ubiquitous. Apart from helping people in their day to day repetitive tasks smart assistants will eventually integrate into our lives as our alter egos. But that might take couple of decades to become reality. 
Other take is how smart assistants can help us in learning new skills or moderate our actions to improve existing capability?  In today's world smart assistants can certainly act as information or knowledge provider; can they go beyond and play the role of personalized coach? Well, the answer is yes; with growing power of data processing and advancements in ML and AI techniques smart assistants are poised to take up such roles. By leveraging right sets of data we might be able to infuse capabilities human personas of champions of fields in the smart assistants. Imagine someone is working on a presentation note that needs to be rearticulated, and he takes the help of an assistant which is powered by Shahi Tharoor like grip in English language. Or a guy preparing a desert recipe gets culinary suggestions from Sanjeev Kapoor; or a young tennis player is getting backhand shot tips from Federer.

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December 12, 2018

Resetting Robot's Dream

"Cal is a helper house-Robot owned by Mr. Northrop, an author and technology enthusiast. Mr. Northrop is a prolific writer and sometimes loses track of other activities, he likes the way Cal picks up after him, runs his printer, stacks his disks, and other things. He doesn't need a complicated robot and Cal surely fits in. But Cal is a special robot with a level of intelligence not completely explored and with time Cal develops curiosity and interest in writing. More like being influenced by the author persona of his master. As Mr. Northrop comes to know Cal's interest he decides to upgrade Cal with dictionary, vocabulary, grammar, and other essentials for writing stuff. Cal starts writing, initially he wrote random letters like gibberish. But with more upgrades and advice from Mr. Northrop, Cal got better and better. After few attempts Cal wrote a satire with perfect sense of the ridiculous, Mr. Northrop read the story 2-3 times; a sudden feeling of insecurity came to him, what if Cal writes more stories and continues to improve each time? Mr. Northrop decided to undo all improvements and reset Cal as it was when he bought. "

Above is the summary of science fiction short story written by Isaac Asimov in 1991. He wrote many stories on robotics and often credited with devising Three laws of Robotics, which was adapted into  Hollywood sci-fi action film "I, Robot" starring Will Smith.

The vision on future of robotic automation and questions raised by Asimov on freedom of choice is even more relevant in era growing practice of AI. The core issue, that may have prompted Mr. Northrop to take the reset route, is his inability to appreciate the robots did and the grey area around robots decision making which is incomprehensible. Recently Facebook was experimenting with chatbots which were to negotiate among each other for ownership of virtual items, but after a few rounds the AI programs seemed to be interacting in a language that only they understood; Facebook had to shut down the experiment.

Transparency is a major factor that we need to address for building sustainable AI systems, in above case had Mr. Northrop knew that Cal was only trying to mimic him for extending help rather than being a competition, his action could have been different. Along with that interpretability and explainability of decision taken by AI systems would nullify grey areas, thereby building confidence among user community on trustworthiness of the systems. The factors will be crucial as organizations sail through the transformation journey of industry 4.0 where AI will have significant penetration across industry verticals.

To stay ahead with the AI curve, Organizations must build trust in their AI application. That will also speed up adoption of AI application among the stakeholders within and outside the organizations. For example, there is huge potential for AI in banking sector. In areas like traditional loan approval value chain from application to disbursement, AI can be applied at stages such as validation, due diligence, and approval; but lack of trust & transparency in AI applications hinders the adoption of AI led loan evaluation process. There are many such cases across industries like customer recommended in retail, optimizing the distribution of energy, fraudulent reimbursement in insurance etc.

Moving on to digital era we will be surrounded smart AI systems and would interacting with real life CAL s for day-in day-out. So, it's our imperative to build robust mechanism for explainability as well as trusted and sustainable AI systems.

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December 7, 2018

Rise of Emotional Intelligence in AI

We typically prefer to be with people who can understand us and are emotionally intelligent. Body language and tone play a significant part in what we think and feel. Emotional intelligence encompasses the ability of people to recognize, understand and control their own emotions as well as recognize, understand and influence others' emotions. EQ has become an important consideration when we talk about AI development. As per Rana el Kaliouby, co founder and CEO of Affectiva, an MIT spinout company that works on emotional recognition technology, "If it's interfacing with a human, it needs social and emotional skills." The addition of EQ to AI will help such systems respond better to more complex human needs leading to creation of better customer experiences and thereby improve customer satisfaction.

Businesses are increasingly benefitting from advances in emotionally intelligent AI as they uncover new opportunities by understanding consumer likes and dislikes along with gauging their affinity towards a brand or product. As per a recent study by Market Research Future (MRFR), the global emotion analytics market is expected to reach USD 25 billion by 2023, growing at a CAGR of 17% between 2017 and 2023. Also, Gartner predicts that by 2022, 10% of our personal devices will include emotional AI capabilities, up from less than 1% in 2018. Using sentiment analysis to understand consumer perception towards a product/brand in the offline world has remained a daunting task. Detecting emotions from facial expressions using AI can be used as a substitute to better understand consumer preferences and how they engage with particular brands.

Traditionally market research companies have relied on using different methods such a surveys, trade interviews to better understand consumer requirements. However, these methods assume a direct correlation between future actions and what the consumers state verbally, which may not always be accurate. In this scenario, behavioral methods are considered more objective and are often deployed to observe a user's reaction while interacting with a product/brand. Manually analyzing video feeds of users interacting with a product/brand can be pretty labor intensive. Facial emotion recognition can be useful in this scenario as they allow market research companies to record facial expressions automatically and derive meaningful insights from them.

Disney has designed an AI-powered algorithm to gain a better understanding of how audiences enjoy its movies, this algorithm can recognize complex facial expressions and also predict how audiences will react for the remaining part of the movie. As per reports, the tests processed a staggering figure of 16 million data points derived from 3,179 viewers.

Earlier this year, Soul Machines partnered with Daimler Financial Services to present "Sarah", a digital human as an interface to Daimler's financial services and mobility ecosystem aiding them to deliver enhanced customer experiences in the areas of car financing, leasing and insurance by utilizing facial gestures and natural voice intonation.

Annette Zimmermann, vice president of research at Gartner claimed in January 2018, "By 2022, your personal device will know more about your emotional state than your own family." Facial analysis, voice pattern analysis and deep learning when used together in conjunction can help decipher human emotions with applications across a broad range of industries such as retail, financial services, medical diagnosis, autonomous cars, fraud detection and recruitment among others.

The shift from data-driven interactions relying heavily on IQ to EQ-guided experiences will also present companies an opportunity to connect with customers on a much more intimate level. However, emotions are immensely personal and companies working in this space should be wary about consumer concerns such as intrusion of personal space and manipulation. Suitable psychological training for people is also required to interpret emotional results from these machines and fix deviations as deemed appropriate.

November 30, 2018

Explainable AI - Introduction and applications

AI systems have essentially remained black boxes, with deep learning models frequently remaining opaque. It has become imperative to build systems which can justify their decisions, very similar to how humans operate. Significant advances in this area will result in the evolution of autonomous systems that are able to learn, make decisions and implement them without the support of any external agents. Explainable AI (XAI) is artificial intelligence that is programmed to describe its purpose, rationale and decision-making process in a way that can be understood by the average person. Powerful algorithms often churn out useful results, without explaining how they arrived at it. Thus, transparency is often compromised while arriving at sophisticated experimental results using AI systems. As AI models become more complex, it is important for these systems to provide verifiable explanations of the decisions they make. Key benefits derived from the implementation of XAI include:

·         Aid in faster and broader deployment of AI

·         Bring convenience and speed to consumers along with building trust

·         Adoption of best practices around the areas of compliance, accountability and ethics

·         Reduce impact of biased algorithms

The figure below illustrates the concept of XAI as demonstrated by Defense Advanced Research Projects Agency (DARPA):


Explainable AI.jpg

                                                                   Source: XAI Concept by DARPA

AI systems have multiple applications across industries. For example, in the financial services domain it will be important for AI systems to be able to explain their decision making in order to be fully embraced and gain trust in the industry. If a loan application process is denied by an automated system powered by AI, bank executives should be able to trace the decision to the specific step where the denial occurred and also provide a reasoning for the AI system's decision at that particular step.

An AI system which is determining the premium charges for car insurance should also be able to provide the rationale behind such a decision based on several factors including age, gender, car type, accident history, address, mileage etc. It should also aid in providing a personalized experience by mentioning what the customer needs to do in order to reduce premium charges, for example drive accident free for the next one year.

An ethical risk is also prevalent in this scenario as bias can unintentionally creep into algorithmic models and thereby result in discriminatory practices. This puts organizations at risk as consumers are likely to switch brands once they understand about these prejudices. For example, certain existing AI algorithms imposed higher charges for Asian Americans opting for SAT tutoring. Facial recognition software is being increasingly used for law enforcement and is also promulgating racial and gender bias. Earlier this year, Joy Buoalamwini from the Massachusetts Institute of Technology showed that gender-recognition AIS from IBM, Microsoft and Chinese company Megvii were able to identify gender from a photograph for white men with an accuracy of 99%. However, this number was staggeringly low at 35% for dark-skinned women. This poses increased risk towards false identification of women and minorities.

Explainable AI will thus help to build models which can identify relevant stakeholders and the information they require about how the model arrives at decisions. This would also identify any form of bias which has crept in and aid data scientists weed them out at an early stage. Eventually as humans and machines work together more effectively, it will be imperative for us to understand the machine logic lying underneath.

Transparency will become an important requirement to keep up with compliance regulations. For example, the General Data Protection Regulation (GDPR) with a focus on right to explanation mandates that users should be able to demand data behind algorithmic decisions made by recommendation engines. This puts the onus on companies to translate complicated reasoning behind AI algorithms to simple and easily interpretable language.