Digital sales automation for Financial Services

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good afternoon everyone and good morning depending on where you are welcome to today's webinar on how to scale automation in banking financial services and insurance with machine learning and RPA my name is Ed Wacha whit's a VP of business development automation edge and I'm glad to be joined by our CEO and co-founder with a barrage da and we'll do a little bit of housekeeping so all lines are presently on mute you can certainly have the opportunity to submit your questions through the go-to meeting panel that's located on the right hand side as is illustrated and with that let's get started the agenda would look like digital digitalization trends in banking and financial services and insurance how to scale automation enterprise-wide will will show you some use cases and as far as automation edge it's our intelligent automation platform and that will go into a bit of a Q&A so with that as far as some of the trends that we see within this sector certainly one trend is the interest from the c-suite right from the CIOs from the CMOS and even the chief digital officer x' are very much interested in rpm is machine learning ai etc this could be from within the sector the reliance on legacy systems or possibly the lack of enterprise-wide integrations we certainly believe that the aspirations or to get these firms to be more agile by getting new business models new revenue models via virtual assistants etc so the journeys really starts out with a standard use cases which the key drivers were really all about operational efficiency and cost reduction and then as you move to the value add use cases then you know again standard use cases were there for quick wins and now evaluated use cases are there to extend be cost savings and operational efficiencies things like organization agility improving customer experience service delivery and I also add employee satisfaction it's also about reducing the turnaround time for these enterprise workflows and them and helping you know organizations be more streamlined and then as you move into our pa+ this is where you get to to the intelligent automation point where you leverage machine learning natural language processing key decision-making if you will and inclusive of multiple technologies like OCR like the virtual assistant etc now somebody's extended use cases or and in different categories like you know kyc know your customer assessments and AML checks where the boss can actually go out into disparate systems to get different data to understand your customer can also go into third-party sites where can get a ML type detail and data to complete that AML process another example can be data data reconciliation whether it's around invoice or taken to two documents and reconciling it from financial point of view as well as chase of processes like chasing premiums or or claims if you will and then as you move on to or move into the value-added use cases there's also some very interesting you know cases that take it a little bit further like customer center customer ID their call center customer ID verification where your call agent can actually type in the number that he or she receives from the customer on the phone and the assistant bot can then go out into disparate systems grab the data and presented to that agent while he's got the customer on the call so one example can be showing that the the notes the last conversation another example could be you know showing the products that this customer has acquired from the institution and and possibly help introduce new products to talk to the customer about and if that conversation moves forward then you can automatically enter an entry into CRM system so that your sales folks can then follow up through a reopen entry within your CRM system you know also regulatory monitoring our changes to regulatory requirements change constantly so the bot can actually go out to these sites that have regulatory bodies that has these regulations or bring them down into your system compared to the less a regulatory statement and look for the Delta and therefore you know you don't miss by reading these extensive documents and the bot can help you understand where that Delta and where that that a change was also could be grabbing data from from different sites on your customer and so with that and the privacy you know you can create that the agent or your digital assistant to become GDR our GDP our compliant and then as you move into the off ba+ this is where you know you start to create skill based tasks right support for unstructured data machine learning comes into play as well as complex decision-making so if you take the example of email triage you know any mail can be sent from a customer for example asking for a statement for the period from January to March with machine learning and intelligence the digital system can actually parse that email and whether the request is coming in from january from march or from oh one - oh three the the gentle digital system can can understand that and then bring out the e statement and send it back to the customer you know did you say the digitalization of structured papers form this is where the combination of technologies really come into play from a OCR when if you are long with RPA to give you a you know a great basis for intelligent automation some of the key challenges that certainly the sector's facing is you know staying competitive against your digital newcomers right this requires you know the banks and the financial services in the insurance company need to embrace and expedite digital transformation you know some you know organizations are very innovative some like to sit back and watch and wait for others I believe you know within our PA and as well as IT process automation it's not a matter of if it's really a matter of when do you start that journey and within within this sector is huge numbers of manual processes so how do you take the how do you take the manual process automated quicker turnaround time less errors 100% availability because the bots are running seven twenty four you can also look at you know multiple outdated systems you know the swivel chair effect if you will by leveraging automation our PA in our orchestration layer to integrate spirit systems is a great way to to help automation get realized within an existing environment so there's really two approaches that you can take yeah you can go shallow wide angle shallow meaning you can look at different processes and different departments you know and you know try to get a quick wins adoptions it could be within underwriting it could be within it with the renewals things in this nature where the other approach is really to go narrow and deep so take a Michigan mission critical process to a business let's say you know claims processing or or you know payouts if you will and who day will show you a great example on how we helped organizations with their payouts for mid levers so where wine is shallow could be a subset of a number of processes or a number of steps within a process when you go narrow and indeed that's taken a an entire end-to-end enterprise process which could have you know 20 70 150 steps within a given process and and automate and automate at that point so with that I'd like to turn it over to today to take it through the next part of the presentation thank you you thank said we continue from where it has left so automation age is a platform for digital enterprise and we always had this approach that RP can definitely help in the automation journey but beyond RP a technology are also essential for enterprise wide automation so if you see here automation inch being RP a is just one quarter of the beat and other is about I t's automation so so these are these sections are useful for for what I'd talked about is about the wide automation and if you have to kind of go beyond that is where there are low-hanging fruits where you want to go deep and narrow you need to have essentially other technologies and that's about one of the important technologies the data and ability to handle data efficiently having an ability to do ETL or big data connections and about the OCR other important technology is machine learning AI and this we have made it available with the product and we don't have any dependency on cloud to kind of access this technology so so when these combination of technologies available with you it's possible to simultaneously approach the deep and narrow use cases which make big difference to the organization as well as low-hanging fruits and enterprise-wide automation with relatively a shallow approach but many many processes being automated in the organization combined with this is our ability to do rapid API integrations and build those reusable components allows our partners and cost contribute into the bot store it may be enterprise specific bot store or something as a marketplace which my automation edge has and partners can contribute there that kind of allows is go beyond the screen based and screen stepping based automation but build the components and use them many many times this is about our PA technology and our PA technology essentially took the path of achieving automations using the surface of the screen of automation screen of applications instead of using api's and BPM and SOA for every scenario which was not justifiable in terms of ROI and that's why there are many humans sitting there and doing a slower action to take input from one system in apply certain rules and enter the information into another system what we provide as our PA technology is a combination of object based approach to build the workflows and as well as the smart recorded recording feature the smart recorder allows you to build a granular workflows which more look like flowcharts but essentially build the automations which is maintainable and granular enough in terms of benefit of adopting our PA had talked about it the opportunities which are there essentially by doing automation and using our PA technology what organizations are benefiting a the essential things why they are kind of driving these initiatives in the organization one is about the revenue uplift growth of the revenue cost optimizations customer experience as well as regulatory compliances and reduction of errors and all of these now I will talk some of the use cases and case studies which essentially will give you an idea in terms of our PA and AI and how it is used in the industry and how it can benefit organizations so this is one of our customer they started their automation journey one of the initial cases they did is help them grow their business essentially these email BOTS and under the technology including our PA has helped them grow into the market which was otherwise econo canonical for them to pursue and that has helped them to go into SMB market where the the turnaround time and the margins or the cost which are basically very very competitive scenario and in that case they would not have able to operate in this market with the traditional approach of a large sales team and a longer time of wait for customers with this technology they are able to reduce both in terms of the manual effort as well as reduce the turnaround time significantly from five days to let's say five minutes from there they basically went enterprise-wide and their approach was to kind of attack a low-hanging fruits and expand rapidly so they could kind of take it to different departments and different business units including fire motor crop or other areas of BNC business and in terms of the different processes they automated including the quotation processes the actual issuance of policies to claims or no claims bonuses to endorsement and customer connect for renewals and such things this or this other organization they took the other approach and they are into life insurance business they started with the use case which was deep and deep and really narrow narrow in the sense that it was kind of specifically focusing on payout as the area and how is that a moonshot can be achieved wherein it was otherwise was difficult to kind of think about and achieve into an automation and provide a different level of user experience for the customers who are even when they are middly worse as far as the insurance policy is concerned how is that that experience can be great and they will return whenever is possible for those those customers so so in this case they could reduce the time of quotation from three days to few minutes and as well as the payout process itself used to take seven days now it kind of happens in two days that two days is also because there is a dependency in terms of stock market closing and enable value calculations but it's essentially is a very very high impact financial financially impacting process they are done around 1.5 billion dollar worth of payouts after this automation using the the RP a BOTS and and essentially kind of provide that experience which they started with as far as customer experience is concerned effectively and then they took the journey further and when they kind of went ahead it was enough was proof there in the organization right from the top management high visibility and with the graded achievement and they could rapidly go wide in the organization and actually essentially both assisted and unassisted bought based automation and they could go into many different departments including the group operations or underwriting or claims and central operations and all these different areas and could use this into in the different areas including the KYC group insurance on/off and other such areas now in terms of banking this customer their focus was in terms of how how do they provide a digital experience at the workplace or is that the business users experience from IT can be significantly improved in terms of customer start and turnaround time and they initially started with is more about end user support processes including password resets or user account unlock shareholder accesses to the employee moves and kind of effectively providing access to different systems from there they went on automating other areas including data center and networks and automation of start and when they did this these automations of the IT side they reach to a level where they are able to solve around 10,000 tickets every month both the incidents and service requests using automation and from there they have expanded in other areas of business operations including the the payments as well as central operations in the back there are there low-hanging fruits in the banks which can basically be attacked first is processes like Bangkok enclosure soldiers and for example this particular Bank they had 25 folks required to work every day in terms of the conclusions and account closures for different types of bank accounts whether it is d-mat accounts or about their checking accounts for savings accounts and many different accounts they had customer accounts they had errors earlier and huge turnaround time and exception handling scenarios so from there when they automated this process they could save in terms of these many resources and and the turnaround time improved significantly and accuracy as well and they could reduce those errors and basically successfully handle the business and system exception scenarios so there can be many such processes where which are rules based and essentially can be taken up for wider automations in the organization but there are many of our customers pushing the envelope further they are taking this technology and kind of using the the the machine learning AI and NP capabilities available with the product and helping them gets all the some of those challenges which are not possible to solve just by rules based approach and for example here this is about their support desk where there are support at XYZ comm and such addresses for these banks and insurance companies they keep getting a complaint emails and request emails and things like that and essentially all these emails are unstructured documents or unstructured text and what what automation is provides here is an ability to combine our PA workflows with machine learning and AI workflows within a single process studio for development so so when the bots they can learn from the past data how humans have solved these kind of tickets and the request and the request can be like a customer may ask for a statement of account or they may ask for the document status or for an insurance company's customer may ask for the change in address or change in contact number and such requests and they are very high in volumes and if humans are put in that task it's basically essentially there is a backlog pile and pile up happens or in terms of the turnaround time the customer experience is not great but with this approach God can learn and they can recognize the intent and entity when it received the the bots receive a new email and the approach can be three different ways it can be solved one is that if the information is understood by the bot it can send out a response in terms of the additional information which is needed from the customer or it can do is end-to-end recovery or resolution of that request by by understanding and getting the intent and entity going to a back-end system and using the RTA pulling that information for example getting a statement of account for that customer and applying the the password and then protection and digital signature and sent back that particular document to the customer within few minutes or if it is not possible to get all the information there can be a partial remedial action in terms of the essential documents being pulled up and then it's handed over to human to look at and fill the additional set of data points before the board can go and pull the required information using our PA and send that back to the customer so so this is the way it can kind of go further in terms of the enterprise wide automation and this this technology can be used in many many different ways and I will talk about it further in a few of the slides at automation edge what we are providing to the financial services industry is not only just a pure RP technology but but these ready automation BOTS for example here for insurance they are built using the solid building blocks which are available with automation aids as far as an LP machine learning ETL capabilities or strong reconciliation capabilities ability to handle excels really fast at a 10 X speed name matching and other such support which is available in the product and with that for example in in case of new business the technology can be used and these ready parts can help in terms of the cross cell within the new business unit for them to kind of attack new opportunities to gain the business from the existing customers the other areas like it is shown here for example in a group policy these ready workflows can be used for different scenarios or different requirements for example when there is a group insurance policy and in terms of servicing there are scenarios where that process is called as on and off where the customers and the employed Attah and that needs to be entered into the core system and before entering it that that information has to be converted into a standard format so there are these bots which allow you without coding to convert any kind of information coming in into a standard format and then over and above that you can basically have is a group underwriting kind of a part which can learn from the past data in terms of how humans have done the occupational class calculations and that is the bots can learn and build their model and then the the prediction step can help you arrive at the occupational class for the new set of employees of the customer and then essentially arrive at the underwriting and the commercials which should be given to this customer so so like that there are many many different areas in reconciliation for example can basically be the these ready parts can be used the same way we have built it for banking these ready parts and these workflows and and those are used by and can be used in different departments and different business units including retail banking corporate banking wealth management or in governance and risk areas so as far as the machine learning is concerned it can be used in I talked about support desk but but these prediction steps which are available with the product and the morale building step allows you to use this take the machine learning and use that into the workflows without needing to know basically having deep understanding about how to use different algorithms available out there you they're very flexible and you can provide a different type of data to these these model building steps and and essentially can you can use that in customer churn scenarios or risk or default and many such scenarios not only that we have provided the ready to use prediction steps but there we support our and Python within the platform so your data science team can build their own innovative solutions in using AI and ml and use those within the RTA workflows you can automate every department whether it is front of his back of his middle office or IT operations and use it in different areas including ERP automations account payables and such things as far as automation is concerned we have 30 odd million large size banks as our customers and financial services and insurance companies and they have used automation edge both for the use cases for enterprise wide automation as well as a deeper and very organization differentiating use cases in their their companies seven of global 2000 companies are using your product we are the product is mentioned in both our PA as well as IT process or automation market guide a unique position that we have with that I would like to open this session for Q&A if you are and or do you add yes so please feel free to send your questions in and delicious when you receive them please the Mina so so you how it window available with GoToWebinar so you can type in those questions and we will be happy to answer them okay so we just got one in who they what are some of the KPIs that you can mention as a result of adoption of intelligent automation and especially around IT process automation so thanks for that question firstly the combination of our PA and IT process automation working in concert brings the ability to accomplish an end-to-end process for either a narrow and deep approach or a wide and shallow approach so with i2 PA you can you can achieve set up 70% cost savings right the combination of both at our PA an IT PA ensures a reduction in IT operations cost which can then be utilized to fund other IT transformation products there are certainly increase productivity improvements because IT process automation aims to ensure the reduction in downtime of these mission-critical revenue-generating apps and the improvements in SLO s the service level objectives which are a key element of SLA s between IT and the business IT also so the next question is how do you choose which approach to take whether the shallow and wide or deep and narrow in the organization as far as our PA initiatives concerned depends the way the organization setting is and risk appetite which is there we have seen both the approaches work depending on the type of the organization and I described those those characteristics are the organizations where it has succeeded so so we have seen both the approaches and the narrow and deep is essentially about the wait time because these these use cases may take a little longer to go live because it's kind of brings in lot of difference to the organization they might have a great financial impact on the organization so so there is a lot of new ad and technologies basically coming together and and kind of making them work so so that can take a bit longer as against the shallow and wide approach where you get is you can attack the low-hanging fruits and should show the results in case of deep approach it's kind of it gets a lot of attention in the organization and further journey becomes really smooth and you can really go wide as well not necessarily always have approach of going deep but yeah it depends the question is how does the automation edge connects to the enterprise systems and the UI or otherwise approach so we have four hundred-plus ready steps to connects to different systems and ready pods and there many times when they are reusable components they essentially are based on either the core system components of automation edge or they are API based or a back-end based connectors to different systems and they are stable over the longer period as far as the target system changes are concerned and much more easier to maintain and to drag and drop drop approach so they are connectors for example Salesforce ServiceNow or as your office 365 and many such systems the next question is are these plugins for example for insurance or for banking these ready workflows are they customizable for example in the underwrite area or in the group insurance policy areas the answer is yes so these workflows are available to you and they can be used in the different scenarios and they are generic enough and if you want to kind of modify for your own organization set up and customize it or basically essentially provide a different data inputs to those steps that is possible yeah so the next question is tell us about our PA from an ETL standpoint right so when you are dealing with different use cases many a times the use cases involve a large amount of data for example reconciliation steps or fraud detection steps or when you have to do is deduplication with your customer master data so in those scenarios if you are able to handle the data efficiently or at a speed for example even an Excel processing which is large enough which has millions of records or those records are in database if you are able to handle using the ETL technology essentially you are able to handle that data at a really high speed 10x faster and with that you will be able to achieve the results of automation much faster and that might that would be many times of essential need of the hour - hu the automation and turnaround time much faster and if your data processing is going to take a lot longer or a development time is lot more then it defeats the purpose of automating that kind of use case so ETL technology along with our PA is is very very important you would hear some of the large banks talking in some of the forums that how they built is they purchased our PA from one vendor ETL from another vendor and then built a team for machine learning and that is how they could build a smart technology and and then could scale all the heights of all the mountains and probably they will do a desktop automation one day that's the approach is if you aren't kind of really scale up you need the combination of the technology which I have talked about in automation HP next question is is analytics and reports built in with the product yes so so we do provide ability to we do provide canned reports as well as you can build your own custom reports and specific to your business units and make them available in the reports and dashboards and those can those are very very handy to use it for the business operations team and IT operations as well as for the management reports and dashboards right so how does machine learning help and risk management and do you have any examples so machine learning and is for example the group insurance policy and in that if there are human errors and the risk can get introduced in in terms of arriving at the premium calculation for a large number of employees for an organization when you are arriving at rather than just going by a human judgment and calculations is that this machine learning can help it can learn from the past data how a human humans have done the occupational class and classification and and based on that the bots can come up with their own classification and occupational class for new type of organization for a new organization and employees in that organization and that can assist and reduce the risk in terms of underwriting okay with that we are at the top of the allotted time for this webinar and thank you all of you for joining this webinar and look forward to meet you again in the next webinar thank you