Hi friends. Welcome to this 2019 update video. This video talks about various industry examples of innovations in general corporate world as well as digital banking area. You can choose to go through this video either at the start of this course or at the end, as per your own choice. Do let us know whether you liked this video. Our innovation themes in this video include artificial intelligence, machine learning, chatbot, IoT, blockchain, big data, mobile banking, Internet Banking etc.

Let us begin our journey with few interesting examples. As you know, we human being have taught computers to generate artificial intelligence. And this is like when your mother taught you meaning of Apple flower or a puppy when you were a kid. This learning by computers is similar to traditional artificial intelligence. However, when you learn on your own from this planet store everything in your mind. That phenomenon is similar to machine learning, deep learning or reinforcement learning by computers.

In 1996 chess grandmaster Garry Kasparov beat IBM Deep Blue machine in a game of chess. But immediately next year in 1997, IBM Deep Blue beat Garry Kasparov. However, IBM had put extensive research for eight years to come close to Garry Kasparov super brain power. Those eight years were spent in researching and teaching various strategies and patterns a chess player with highest IQ can probably execute. So IBM's Deep Blue was using traditional artificial intelligence which guides every rule, pattern and strategy to a computer. However, post 2016 Google's AlphaGo zero can beat any chess opponent with highest IQ on the planet within just four hours in 1996, when IBM develop Deep Blue chess game that was using traditional artificial intelligence.

Whereas today's programs from Google, which beats opponents in four hours is called as machine learning or deep learning. Therefore, the latest versions of computer programs learn millions of patterns, strategies and various rules and evolve the best way to beat best human brain in just four hours. Therefore, when a computer learns all the rules and regulation of a credit evaluation process in a bank, and feeds all those rules and patterns in its memory, it's similar to using traditional artificial intelligence. However, when computer learns all those rules of credit evaluation, and also auto learns all the exceptions which credit officers have utilized beyond the stated rules, it is similar to the incremental enrichment in machine learning. Higher stages of incremental learnings concepts are also called as deep learning or reinforcement learning using traditional Artificial intelligence, machine learning via metrics and risk analytics. A bank in China called my bank has created a theme of three is two, one is to zero.

And what is three is two one is to zero. It means that any small and medium business entrepreneur can apply for a loan within three minutes. Their loan gets processed in just one minute and this loan is dispersed in zero minutes. In fact, 30 million SMEs and farmers in China will get advantage of three is to one is to zero scheme in next three years. In China, artificial intelligence, machine learning, facial recognition and big data has been used to store access and analyze data of billion plus Chinese people. China is on the next level of evolution as they are developing social scoring which is much more intense coverage than financial credit scoring for each person in China.

Since 1960, scientists could send satellites to the space why space flights travel in the space orbit, a ground control team can always control and track satellite navigation. Therefore, ground control team has replica of every movements of the space vehicle in the sky. We can call that as virtual twin of the real spaceflight. In the digital era, a human being has invented similar concept called digital twin. While you visit any mall, companies like Google can track you extensively. However, expensive tracking of space shuttle in form of digital twin has been now replicated with much cheaper but effective digital version.

Let's take an example of a company called Experian, which provide credit scoring for individual customer of banking sector. Experian is experimenting on a concept of digital twin for analyzing every individual they deal with. On a Singles Day on November 11. In 2019, more than 18 million Chinese people use facial recognition called smile and pay at a company, which is part of alibaba.com facial recognition help them to make a payment without swiping of any plastic card. This was a new era of alternative form of transaction processing in just one single day. banking industry is evolving a new business line which is replacement to plastic card and can be offered in the form of line of credit.

In this digital era, banks can offer credit to customers without use of any plastic card. And this transaction can be authenticated by use of biometrics. A very innovative financial wellness company in USA called movin has created a product called emergency cash, which offers emergency credit to customer once he or she enters shopping mall. This banking product uses GPS, artificial intelligence and financial behavior tracking which helps customer with immediate credit if they are out of cash or credit. let us study various use cases and examples where banking industry used artificial intelligence or machine learning. We shall use abbreviations such as AI NML.

For artificial intelligence or machine learning Henceforth, many banks offer credit card reward points, based on which a lot of freebies and gifts can be given. This ensures customer relationship, customer connect and helps banks to develop their customer friendly brand. However, many times these rewards are not utilized by bank. Therefore, HSBC used a IML technology to increase the reward points utilization, HSBC and monetize motivations have created a IML driven study to find behavior of customer over internet and call center. HSBC targeted specific emails offering either of travel, merchandise, cash or gift as reward point for credit card. This is It was very successful when they got 75% response on the 75,000 targeted emails they sent to customers.

Previously, the success rate of reward point usage without a IML was less than 50%. banking industry need to spend billions of dollars to avoid frauds from the hackers and fraudsters. Machine learning plays a huge learning as it automatically accumulates and tracks new type of frauds not stored in computers before. Citibank has partnered with a tech company called feeds eight to provide security against fraud for various domestic and cross border payments across the globe. In a split second, Citibank is now in a position to identify, monitor, track alert, act on frauds from millions of data points using feeds a state of the art machine learning engine. Managing your personal money has never been easy.

A lot of people lose their hard earned money without enough financial education. Therefore, many software companies have developed personal financial management software to help investors in growing their money and to achieve various financial goals set by the individual. Companies such as Yoli have developed a AI based advanced personal financial management engines which takes inputs from thousands of sources, study market patterns, study customer own goals and usage patterns, and recommend best portfolio solutions. When banks offer loans to customers, credit scoring of a customer is obvious. However, with millions of new customers without any readily available credit score, especially for small and medium business owners, banks find it very difficult to evaluate a customer in split seconds. With the help of social scoring in China, and financials, a subsidiary of alibaba.com is in a position to do credit scoring in less than a minute, studying more than 12,000 patterns of customer behavior.

Ai and ml technologies are extensively helping for underbanked people with no ready scoring available with traditional financial scoring companies. This has created a new customer base on millions of customers which did not take loans from traditional banks, but depended more on private money lenders, using Robo advisory services and utilizing the power of AI NML. A Singapore based bank called ocbc is offering new services to manage the investment portfolio of stocks and bonds. This auto advisor demonstrates 28 different ways in which investment portfolio can be structured and asked customer to choose investment pattern of one of the financial models from 28 listed models based on customers preferences and attitude. However, it does not end with the selection of a financial investment model by the customer, but goes on to assess and track the performance of the investment portfolio with regular frequency. As per the Need.

Robo advisor also suggests reshuffling or restructuring the portfolio. This ensures customer to always remain alert and achieve best results within the investment horizon. Various banks are now using chat bot to communicate with millions of customers. chat bot uses various technologies such as artificial intelligence, machine learning, natural language processing, big data and analytics augmented reality and virtual reality. Let us see how some banks are using chatbots Commonwealth Bank of Australia has developed 200 different tasks on chatbot called CV if we're responding to customer CBA has included simple questions such as Tell me about saving product of CBA or tell me your balance to transaction such as make a fun transfer. This event chat bot is now available for 6.2 million customers of CBA USA which provides financial services To military community has developed chat bots which can recognize multiple accents and also messy communication.

Using natural language processing. us a chat bot can identify spending patterns history of individual customers as well. Erica, the chat bot of bank of america is one of the best examples of intelligent usage of chatbot. Erica not only assists the normal banking transaction, but also guide customers with the financial advice. For example, Erica can advise customers to transfer excess money in his savings account into a demand deposit account. While advising a higher return.

Erica also guides customers on typical monthly expenses based on his previous expenditure pattern. Erica is one of the best industry examples of using AI ml natural language processing, big data analytics including predictive computing etc. Nowadays Banks are helping customers not only for providing financial advice, but also proactively give best discount offers while you're buying shoes or garment in a mall. This essentially means that banks use GPS to track customers using AI ml to assess customers shopping trends. use big data with predictive analytics to anticipate the future expenses trends, and they have tie up with multiple industries for offering good discount if customers use banks credit card or bank account. Therefore, mobile or internet banking has changed from earlier time reactive web portal with history of transactions to better financial advisory and value creating web real estate.

A bank in Spain called BBVA has developed friction less invisible payment whereby the customer can plan his visit to the restaurant by booking his seat, ordering for food from his mobile collect food on the counter as soon as he reaches the counter. customer can split his bill on the spot in case There are two or more people. Also, facial recognition software ensures to identify a customer and automatically debit to the amount to the account or credit card of the customer. Therefore, the whole experience is friction less as customer do not have to wait over the counter for food or making the payment or splitting the bill. Banks are now helping customers not only for providing financial advice, but also proactively give best discount offers while you're buying shoes or garments in a mall. This essentially means that banks use GPS to track customers using AI ml to assess customers shopping trends.

Use big data with predictive analytics to anticipate the future expenses trends, and they have tie up with multiple industries for offering good discount if customers use banks credit card or a bank account. Internet of Things or IoT is taking world by storms. Therefore, self driven car Smart Home drone based farming, automated courier services are changing the way in which this planet is reshaping its journey. banking is behind the curve in usage of IoT. However, time is not too far when we shall see full fledged IoT usage in banking. Let's see some examples in banking of IoT usage in terms of smart beacons.

A smart beacon is a small proximity beacon device that sends a signal using Bluetooth technology. When a smartphone or tablet is in the beacons range, it triggers a notification on the device. When customer is near a branch. Bank speaking can identify a customer and send him or her a contextual signal such as May I help you or beacons can inform various service counters of the branch. In case customer is in shopping mall, beacons can communicate the discounts and cashback on usage of banks credit card in the non banking world. IoT is Already in advanced stage in many industries, however, banking uses very minimal IoT and currently IoT in banks is more confined to the use of smart beacons.

Westpac bank in Australia has started using beacons to identify and greet customers to serve better. Chase Bank and USA can facilitate customers identifying the services they would like to opt. While they near the chase branch. Barclays in UK help disabled customers to navigate to the branch. Us Bank personalizes the entire smart beacon experience to the customer, and Citibank send promotional offers with customized messaging using beacons. Typically, insurance companies price their car insurance premium based on make of the car numbers of years used any accident history and few other risk parameters.

However, to it, software providers from USA have created new IoT based policy which can track the behavior of the driver of the Car using IoT, which means, if driver has driven his car satisfactorily, he may get good discount next year, as IoT based sensors installed in the car give that relevant data. This technology also enables customer to get quick claim settlement in case of accident at the remote location. blockchain is a system in which a record of transactions maintained across several computers that are linked in a peer to peer network. For example, if any purchase is made by importer and he records this purchase transaction in his computer's blockchain concept enables the same record to be stored in computers of many people at the same time. This can include importer, exporter, Central Bank of importers, country, Central Bank of exporters, country, importers, bank, exporters bank, corresponding bank, as long as each of those are in the same computer network called as blockchain this feature in transparency, speed and security of the transaction.

Company named ripple connects banks and payment providers via a blockchain network called ripple net to provide sending and receiving money globally. 100 plus banks such as Bank of America, American Express, Banco Santander, yes bank, RBC, National Bank of Australia BBVA, Spain and Mexico bank, Santander etc have already joined this network for blockchain cross border funds transfers. A digital trade finance platform called v dot trade has already created blockchain platform for trade finance activity and the members include banks such as Deutsche Bank, HSBC KBC Natixis nordia, Rabobank, Santander Societe, generale, Unicredit, etc. ICBC bank in China is working on industry wide registry for KYC Records, which can be shared across multiple constituents using blockchain, Australian stock exchange or a sec has already created comprehensive clearing and settlement function using blockchain. And now as he is on its journey to create blockchain based infrastructure for its stock trading as well.

Big Data is a term that describes the large volume of data, both structured and unstructured. That inundates a business on a day to day basis. These days CIO of a bank gets data not only from traditional sources, such as text and numbers from legacy banking platform or M is reports, but also gets data in form of video, audio, biometrics, social media feed, etc. Big Data technology framework enables people to call it and analyze huge data from all different sources and can provide value from the data so collected. For example, big data can identify the next best possible purchase action of the customer using predictive analytics. Sometimes, you wonder as to how internet can track me and offer me discounts or loyalty coupon quickly, many would say that it's because of big data.

However, big data cannot make wonders on its own. While you talk to a chatbot AI or ml can easily identify your spending pattern or transaction behavior in your bank account. Moreover, natural language processing or NLP can identify tone of your expressions. IoT can track your smart gadgets and using all these technologies, big data can now predict your next best action. Therefore, while Big Data can very easily identify your behavior, your interactions, your future action and instantly give you offers or loyalty coupons. Big Data is never alone.

Big Data always gives you value for money as it collaborates with AI, ml, IoT and LP chat chatbot and many other technological advances using big data data, TD Bank could create a data lake and this data lake centralize the entire customer lifecycle data in one big store instead of multiple pockets. This not only saved huge infrastructure cost for TD, but also helped to optimize its data usage across all the different departments of TD Bank. TD Bank can now run customer centric digital services such as it's my spend very efficiently collaborating with big data, AI and ml. In the times to come, TD Bank will be able to help customer journey comprehensively starting from customer acquisition to customer servicing. This will help its customer relationship and improve market share in the long run. using big data, banks are in a position to analyze facts and reports, which they never dreamt before.

For example, banks can now analyze customer opinion based on expressions shared by customers on social media about their bank. bank can now predict customers next best action much in advance. Big Data can be proactively used for any probable fraud warnings. Big Data can be exploratory. For example, if we change rate of interest, how many customers would redeem their deposits prior to maturity date? Big Data can also determine the customer's lifetime value, or say, how profitable is a specific customer for our bank.

Some of the scamsters in Russia have created financial pyramid or multi level marketing for selling investment schemes. These Ponzi schemes has created a lot of uproar in Russia as common investors tend to lose money in a weak link of money chain. Central Bank of Russia is using big data technology to identify such fraudsters and take corrective actions as early in the lifecycle as possible. With this, we come to the end of this video. Do let us know your feedback and let's connect again This is voice of David from TDs and Europa. Truly thanks to the speaker, and all of you for listening to this video.