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<h4>Vishy shares,<p>There are various ways in which engineering differs between large companies and Startups. In large companies, there are numerous mature product lines. And then there are engineering teams working in these large organisations, products and projects. To a large extent in silo from each other. This also tends to happen even though the founders don't want it. I have worked on products like BigBooks online and workspace products on Google. These are mature and have been around for many years. There are millions of customers using these products. Given the maturity of the product, you are essentially making incremental changes to the product. Then you fix the issues, you respond to customer pain and you build new features. But not at the same rate at which you would build features at a smaller company. So that is one main difference. </p><h4>Mature projects are a different ball game altogether</h4><p>Of course, big companies also come up with innovations, and new lines of business and they launch new products. I am not talking about that. I am talking about my experience of working on large and mature projects. These projects have been around for multiple years, so this is a different ball game altogether. At the same time every now and then, every few years changes happen in the industry. These changes force you to rethink how you have built your product and how you want to run it.</p><p>For example, when machine learning became a big thing, everyone started thinking about how could they use it. Large companies and startups had to do that, so Intuit did the same thing, and Google did the same thing. Those kinds of opportunities do arise but for the large part, you have mature products with minimal changes. The engineering process maturity is usually very high at these companies because the cost of mistakes is also very high. You don't want to break a product when you have a billion users. For instance, if Gmail is down the whole world comes down. </p><h4>Product-market fit is a must</h4><p>With things like these what happens is that your engineering project maturity is at its highest. I haven't seen this elsewhere than Google, it has set the standard for engineering excellence and maintains the quality and develops at scale, these are the things you get to learn out of that. In smaller companies or startups, product-market fit itself is the question, so as long as you have not achieved product-market fit you have to hustle, be agile and you’ve got to try many things while being true to the mission or vision of the company or the space you want to operate in. </p><p>It is a very interesting experience, you learn that a lot of things that are taken for granted in a large company cannot be taken for granted in startups. Clari achieved its product-market fit a few years ago before I got here. So there is a little bit more stability now but nothing compared to what was at Google or at Intuit. We are still defining the rules of this space, that is what makes it exciting. </p><h4>A leader is the one who ends up defining the game</h4><p>And if you are a leader, you end up defining the game and others play the game but you are the one who sets the rules for it. As of today, Clari is a leader in the revenue operations intelligence space and what's exciting about working for a company like this is that you get to participate with the founders. You get to think about - what is the right way to give out this product, what to build, and which customers to go after. </p><p>What problems to solve becomes important because you will have 10 different problems that will look interesting but we have to pick our problems so that we are true to our strategy and continue to refine our identity. This right here is the most challenging and interesting part that every startup deals with which I get to experience at Clari today. </p><h3><strong>Authored by Richa</strong></h3><p>For more information, please reach out to the <a href="Marketing@purplequarter.com">Marketing Team.</a></p><p>Watch the entire podcast here</p></h4>
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<p>Japan Shares, The way Asset-based lending works is whenever you give a credit you actually look at the value of the asset. Let's take gold as an asset example as we are focused on that. We look at gold and especially jewellery, Indians are sitting on a large amount of gold jewellery. When you evaluate the jewellery, one it has stones in it, two, you have to look at the purity of the gold. What we do is, whenever we look at the asset, we use a lot of technology behind the asset appraisal. For example, you take gold and rub it against a metal, when you rub it against a metal you have to look at the properties and colour change of the metal. It talks about the purity of gold. We take a lot of images to see how the colour changes, and how the transformation happens. We collect the net weight, we gather a lot of data and use this data to figure out what is the appraisal value of the gold. And getting the value right is very imp because from the regulation standpoint to manage the risk, you can't give a loan beyond a certain point, for instance, there is LTV - loan to value ratio. Let's say you have a gold value of 1 lakh, the RBI mandate says that we can't provide more than INR 75000 as a loan amount, i.e, 75% is the LTV. If we don't deter right, two things will happen. If we don't appraise it right either we give more exposure where customers will not be able to pay back and the gold goes through an auction. We don't customers to go through that experience, it is very daunting as there is an emotional value attached to it. And the other thing is, if you don't appraise it right you are not giving enough value to the customer. You don't want to be in either spot, you want to be balanced. You give the maximum loan value to the customer as well as ensure that the risk does not exceed. Ultimately you want every customer to get their gold back, we don't want them to auction it. How we appraise gold is when someone comes to us, we immediately arrange an agent and within 30 mins we send the agent to the customer and this agent takes a couple of minutes to take down customer details like the KYC and whatnot. The appraisal process itself takes about 30-40 mins - we weigh the gold, rub the metal, take pictures, and send the data to our control tower to figure out what is the appraisal value. What we are trying to do is essentially automate this entire process. What we are doing at Rupeek is to make it <b>instant approval </b>so that you don't have to weight for half an hour. Let me tell you something that excites us, our vision to build gold loan machines just like ATM machines. So what do you do with ATM, you walk in, use your debit or credit card and walk out with the money. Similarly, we want people to walk in with their gold, deposit it and walk out with the money. We are about 2-3 years away from that because we are doing a lot of research as it requires automation of hardware, in terms of ML and imagery. It is not there in the world, we are the first ones doing it. And imagine if it goes to real estate assets, it gets even harder as gold is as good as a liquid asset comparatively. The moment you get into real estate it is very different. <h3><strong>Authored by Richa</strong></h3><p> For more information, please reach out to the <a href="Marketing@purplequarter.com">Marketing Team.</a> Watch the entire podcast here</p></p>
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<h3><strong>Raghav says:</strong><p> Let me define my version of what is a tech company…it is a very classic debate and people struggle here because most people imagine tech to be something a company selling, if I am selling data, it makes me a tech company, if am building the next high scale database then I am tech company, which is true, but I think the better definition in today's world is tech is not a product, tech is a process. I believe any domain, any industry, could easily be a tech company if the process they are doing is tech-enabled. Because, essentially what tech gives you is, it will give you speed to scale, which is an obvious thing, but what is not as obvious thing is, that it also gives you speed to innovate. I just have to codify the processes, like as a company how you will grow is, figure out a certain PMF and hence you crack a certain code and codify as a company before you can go further. The best way to codify is to which engineers write. And it actually fuels innovation quite a lot. So having said that, our journey has been such that, we started with a horizontal layer, a thin horizontal line, why because a tech company can't really go deep? You can't control the supply chain, you can aggregate. What we didn't realize, is that just being an aggregate is not going to cut it, the quality, the supply chain, and the structuring of the industry are so broken, that you can't really aggregate the broken product, that is when we kind of started going deeper. That’s when we started questioning why do we need aggregators, why can't we directly go to individuals? It was a drastic move…beauticians, plumbers, and so many actually never have worked alone, they have always been a part of small mom-n-pop shops. Having them move from salaried jobs to the gig economy model was tough initially but we were able to create maximum earning jumps for them. Then we realized how do we increase quality and that's when our process changed into going much deeper, we have invested in training, we invested in logistics, we invested in even the products which get bought like the cosmetics, and spare parts because we realized that have to standardize it. We build our product e-commerce inside us to support that. All these essentially run on tech because it’s impossible to scale, it's impossible to innovate. We imagine a world view, where we are going to continuously pursue innovation; a world view where humans are not needed. That fundamental DNA is what pushes us to innovate, codify processes and stretch a little more. </p><h3><strong>Authored by Pratheek. V</strong></h3><p> For more information, please reach out to the <a href="Marketing@purplequarter.com">Marketing Team.</a> Watch Raghav’s full session: https://youtu.be/eu0tR0-ej7Y</p></h3>
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<h3><b>Guru shares</b><p> Speaking as a professional in the field of talent acquisition, data storage is one of the things that excites people. They want to join us to solve these mountainous problems; when I look at it, it’s a triple-digit petabytes number. We are talking about close to 380 petabytes of data that is on our system across the years since we have been in existence. To put things in perspective for your listeners, if you were to burn this data onto DVD drives and maybe there are listeners who will ask what is a DVD drive. So assuming that they can google it and find it; if we were to burn all this data onto DVD drives and stack them up, it would be the height of roughly 8 Mt.Everests. That’s the kind of data we store. This data helps us in a variety of different ways. Traditionally, what we have done with this data is to help our business become even better at what it already is, which is security! It’s the non-compromisable characteristic of our business. You have to be secure. You do not have the luxury of being secure in a slow way. On the contrary, you have to be secure in an extremely fast way. What I mean by that is, when you are paying on a website, you don’t want that payment to take more than a few milliseconds, maybe one or two seconds at the best. If it drags on for five minutes, you are going to assume that this is not working and you’re going to close your browser window and move on, which means it has to be really fast. If you have to be fast and secure, you cannot do it in a manual way, you have to automate it. You have to use the data in an extremely smart way to make sure that you are making the right decision. The question is when to allow a transaction and when to block it? The safest mechanism will be to block all transactions. There will be no fraud because you are blocking everything. But that is the lousiest experience for a customer because nothing is getting through. So you have to strike the right balance between figuring out when to allow and when to block. For us, in the financial services industry, any company that moves high volumes of money is prone to high levels of fraud. Last year we dealt with $578 billion in payment volumes; any system that moves that kind of money can be siphoned off by those who rely on such frauds for their livelihood. Paypal has its share of people who go to work in the morning and come home in the evening while they try to poke holes and get some money off of Paypal on a daily basis. For us, our current fraud loss rates are less than one-third of 1% which is an extremely small number compared to any other payments company in the world. That accounts for why we are so profitable as a business because we lose very little to fraud. We use this mountain of data, triangulating all different data points about the customer to see which anomalies occur in payment and pointing out what doesn’t look right, for instance - this person usually shops with these kinds of things from these locations, from these IP addresses, etc... but how come today something different is happening? Can we find out more about whether this is an anomaly or if it’s okay? Keep in mind that all of this has to happen in roughly about 200 to 300 milliseconds as you have to say yes or no to the transaction in question. This is one of the areas where we use AI and ML heavily; then there are more straightforward frauds like somebody taking over an account, somebody spoofs somebody, that is relatively simple there is also complex fraud where some buyers and sellers collude with each other to defraud Paypal because Paypal has a unique value proposition that not too many other companies have. In fact, I don’t know if any other company does which is - we protect both our sellers as well as buyers. We have a policy of buyer and seller protection. For whatever reason, if a customer is not happy with your purchase, Paypal will refund the money. For whatever reason if you find, as a seller, you don’t get your money from the buyer, Paypal will intervene and will give you the money as long as you can prove that you did the right thing in sending the product. So this is a powerful value. Now, sometimes buyers and sellers collude to start defrauding Paypal. Finding that fraud is extremely complex and it cannot be done if you don’t have the right deep learning algorithms to detect which set of buyers and sellers are likely to collude with each other. This fraud and security risk has been prevalent for a long time. We are now beginning to also look at the other areas where it can help our business. For example, in customer support, we get roughly about 16 million customer calls every year and a significant chunk of those is actually to find out about - “Hey can I get some details about a transaction that I don’t understand or I don’t recognize?” this accounts for 11% of such calls. So now if we are able to equip our customer support executives or use other channels like chat to prevent this, I would have people not call us right? Because these are things that they can solve on their own easily, that would make it a better experience than having to pick up the phone, call and enquire. So we are trying to use AI and ML to figure out what is the best next action for a customer. How can we get chatbots to make sure that we can give them a remedy even before they ask the question - “Hey I'm stuck!” we can say, “I think you’re stuck here and would you like to do this?” People will be like wow even before I asked, they knew what I wanted. That’s a great experience. The question is, is this even possible? Yes, 300,000 servers! that’s what we have in our fleet, right now to deliver Paypal.com. We have 300,000 servers that are serving traffic from around the world for all the transactions that happen. In these 300,000 servers, any problems could be occurring every second, and some of our consumers could be experiencing a problem. If we were to manually detect and remedy this, it would take an eternity, it would slow things down and we would always be in a fire-fighting mode. So we have moved to use data through instrumentation, we instrumented all these systems, we detect signals that indicate that some problems are probably occurring at someplace right now and we solve them even before it becomes a huge problem. So, there are an innumerable number of ways in which AI and ML could be used and we are only beginning to scratch the surface of how transformative it's going to be to our business. </p><h3><strong>Authored by Pratheek. V</strong></h3><p> For more information, please reach out to the <a href="Marketing@purplequarter.com">Marketing Team.</a> Watch Guru's full interview here: https://youtu.be/65IkCYdJOFM</p></h3>
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<h2><b>On Asset-backed lending</b><p> Japan shares: The way asset back lending works is whenever you give credit, you actually look at the value of the asset. Let's take gold as an asset class, since gold is our current focus, and we will certainly have more assets in the future. When we look at gold and specifically at jewelry since Indians are sitting on a large amount of gold jewelry, you have to look at the purity of the gold. We take a lot of images to figure out how the color changes, how the transformation happens, we collect net weight. We apply the image visual processing to figure out what parts of the jewelry are stone, what part of jewelry is actually the gold. We collect a lot of data, and we use all of this data to figure out the appraisal value of gold. And getting the right value is very important from the regulation standpoint and to manage your risk. You can't give loans beyond certain values. If we don't get that right, two things could happen if we don't appraise it right, either we end up giving more exposure meaning, eventually, customers would not be able to pay back. There's a lot of emotional value attached to gold, if you don't basically appraise it properly, the other thing that could happen is you may not end up giving enough value to the customer, so you don't want to be in either spot. The goal is to be balanced so that the customer gets the maximum value. </p><h2><b>On Data Science</b></h2><p> Japan shares: We have a lean data science team, it is extremely crucial to our success. We have invested in data science across the spectrum in many areas. We use data science to figure out which of the leads are coming to us. We have a higher probability of converting or getting a loan. We are using data science to figure out what is the probability of customers renewing the loan using data science models to figure out what is the probability of a loan going to auction? While it's unsecured lending, 1% of the customers do get into auctions. It may be a small number but anything that goes into auction is a loss to the business and we try to work on that. We are using data science to automate the appraisal capability. So, we are investing heavily. I think there are very interesting things we're learning. One of the things we are debating internally is, if we can predict the gold price. It's really difficult. Gold prices have fluctuated a lot. So what happens is, when the goal prices go down, it really impacts us because customers are not going to go on the sideline. They want to try to come back so that they can get more value. We are trying to figure out at least the moment the gold price goes up to a certain fraction so that we can give customers better products and better loan value ratios. </p><h3><strong>Authored by Pratheek. V</strong></h3><p> For more information, please reach out to the <a href="Marketing@purplequarter.com">Marketing Team.</a> To find out more, watch the entire podcast here: https://youtu.be/5l6khamSrjc</p></h2>
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<h5>Neha shares,<p> Gaming is like any other financial app in terms of the transactions that we are handling. They have to be secured, they have to be reliable and there are a lot of microtransactions happening as I mentioned earlier, when you are playing a game you put money in the system that is an ad cash, when you pay some entry fee to play that game, and then when the settlement after your plate settlement happens & then the right calculation need to trigger to get the right amount of the winnings to the user on his winnings to the user and then you have withdrawal system. So this end-to-end has to work seamlessly, a lot of effort has gone from the tech aspect to have reliable, stable systems to handle scale with passive concurrency, again I am repeating that because that is pretty important in this aspect. So there are mostly 3 aspects where technology plays an important role, one is providing seamless real-time gameplay experience, second is having the trust and safe platform for the players to play because as mentioned earlier that real money is involved in this. Third is having right data insights, so that we can provide a customized personalized experience to a user plus it also helps the team evaluate or get rightful insights to build the system that the customer wants. For a seamless gameplay experience, there are multiple factors that you need to work & take into consideration and build on top of it. One is when I mention real-time experience in traditional systems like if a page loads and a couple of seconds it's considered pretty good but when you are in a game you have to work on the level of micro seconds not just seconds but sub seconds is what you optimize for. So the communication layer that is between the client and server the latency should be optimized how much data you are transferring and what data you are transferring is also critical so you have to optimize till the bite so what amount, what is the size of data you are transferring to have minimal latencies then the client, it's not about backend servers only being stable, secure, and working at scale but the clients are also smart you have to optimize them for handling low internet or low bandwidth issues of flaky Internet and then the battery Optimisation is another aspect that the clients have to handle and work efficiently. Third aspect is how quickly they are rendering, there are a lot of micro animations and state of the game that needs to be rendered by getting messages in milliseconds and the clients have to be smart enough to render it properly to have the seamless gaming experience. There is a lot of complex systems which drive the seamless experience there are game servers to handle the gameplay, there are financial transaction systems which handle your add cash withdrawal then there is lots of user data that is handled by other system and also I'd like to mention their trust and safety factor so you have to build those systems who can process rules at real time to generate any or realize any fraudulent patterns based on how the users are or players are using the app what are the gameplay patterns. So these rules run on this data to generate the right for insights and enable the system to take action. Data Science is pretty critical in powering the systems or giving us rightful insights on the gameplay, as i mentioned that personalized experience to the users you have game recommendations you have personalized offer for the user so data science in algorithm is also equally important for this aspect and it's like when I mentioned the fraud prevention and control systems so they are also using a lot of models to create the right kind of rules and patterns There is a Technology team at every vertical, when I say vertical we have multiple games. Each game has their own tech team and their product teams, marketing teams and these teams work together to achieve or to deliver whatever is required by the user. In terms of tech advancements, we have put in a lot of effort in building these robust systems and the lot of new initiatives that also come in like I will say like instant withdrawals, when we started there were not any apps that were giving in gaming at least instant same day withdrawal for the users. We were the first one’s to have that feature in our app and then if I talk about the kind of support that was being offered to the players so we were the first ones to have 24/7 on call support, that is also part of the trust that you're building, if you're face the problem, if you have any query you can pick up the phone and reach out to our customer service executive and get the query answered and the scale and the system i have been talking about the system, but we scale them to such a level that we now hold Guinness World Record for hosting the largest online Rummy Tournament, where around 1,09000 uses that is the exact number of players who were online same time and we didn't stop there. We have broken it multiple times after that, post that. So that is the scale and the technology the aspect we have worked on. </p><h3><strong>Authored by Richa</strong></h3><p> For more information, please reach out to the <a href="Marketing@purplequarter.com">Marketing Team.</a> Watch the full podcast here,[divider line_type="No Line" custom_height="20"] </p><h4>Watch the full podcast here</h4><p> [nectar_video_lightbox link_style="play_button_2" nectar_play_button_color="Extra-Color-1" image_url="1308" hover_effect="defaut" box_shadow="none" border_radius="none" play_button_size="default" video_url="https://youtu.be/BtHbLNySUGQ"]</p></h5>
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