Fresh orders are the delight of every seller. They are what you practically work for and a new order gives your brand the hope it needs. But what if you encounter fake orders? In a brick and mortar store, you must be mindful of those with less than honourable intentions, filling their pockets with your products, and then making off without paying. As an online business, you might think that you are immune from stealing – however the sad reality is that there are lots of people out there who are trying to do the very same thing. Perhaps even more so, given the benefit of committing a crime from a distance. As your business grows bigger and bigger online, fraud should be the one thing that you monitor on an increasingly frequent basis. Fake orders are the ones that have been placed with the intention of defrauding the seller. If left unchecked, fraudulent orders can cost you tens of thousands of dollars or more, easily. Most of the customers in India prefer COD (Cash on Delivery) as a payment mode because they find it much more trustworthy than paying online. This is the reason that criminals place fake orders. Even if your customer is providing you a valid billing address, even then there are high chances of fraudulent orders. That’s why you need to be on top of a few precautions before accepting any order. Here are a few of them: 1.Verify and Contact through Email to Check if the Email Id is Authentic Verify your customer’s email ID. Most fraudulent customers use fake email IDs to place an order. If you email them and they don’t reply, it means it’s a fake order. 2.Call Them to Double Check This is the quickest way to verify someone is who they say. You can call the customer to double-check the information they have provided about themselves. If the number is unavailable, not reachable or the customer seems hesitant to comply with you, it means there is something fishy. 3.Confirm Order Before Shipping it It is always better to get in touch with your customer via phone or email and confirm once they want the order to be delivered. This way you are sure if the order must be shipped or not. Just in case they do not reply you must immediately cancel the order. 4.Delay the shipment Scammers want their operations to be completed as quickly as possible in order to decrease the chances of getting caught. If you delay a shipment deliberately, and tell them as much, it may scare them off. This is an inconvenience to honest shoppers, thus only use it if you have to. 5.Use A Security Software Software ensures a good level of security for your brand. It permits you to impose several layers of security and makes sure that your customer is authentic and has the right intent. Once your customers clear all security steps, it’s safe for you to interact with them and ship their order. The issue of fake orders isn’t only because of Cash on Delivery orders but also on prepaid orders. Here are few ways to find and prevent fake orders online using a credit or a debit card: 1.Verify the CVV code There is a three-digit code on the back of the debit card, and it is a security measure to prevent cases of electronic credit card theft. If the numbers do not match then it’s most likely a case of fraudulent order, and such an order shouldn’t be accepted. 2.Verify the Address via an AVS system Many people ship the product to a set number of addresses like their workplace or their personal home and those addresses are recorded in your account that you have set up with the portal. This system shouldn’t be viewed strictly as its results could be false positive too. Hence, this method should be used as a guideline. Maximum customers in India prefer COD (Cash on Delivery) that makes it very hard for sellers to differentiate between fake and real orders. Although the above steps are useful in avoiding fake orders, you don’t have the time to do all this for each and every order, hence a good start is to develop your instincts. Learn to identify and spot suspicious orders early, and if something seems off to you — even just a little — by all means, don’t ignore it.

The coronavirus disease or the COVID-19 has seen its preliminary documented affected person in December 2019 in Wuhan, China. Since that time, this virus has been scattering across the globe too fast way too quickly than anyone imagined, and as in step with WHO this disease was declared a pandemic by March 2020 resulting in the shutdown of the whole country. With this pandemic, there were innumerable difficulties to each and every person, and thus not even leaving the businesses. Several businesses face one of the most common difficulties which is brand safety. It is a very common fact that view-ability, readability, and ROI are some of the critical standards and are considered by any brand when buying any ad inventory. Another thought that crosses their mind when collaborating with publishers or advertisers, might be to use the top qualities of the content to get a great brand image. Every advertiser hopes to get the most relevant and pertinent users for their campaigns, but obviously not at the cost of having any unsafe content. And this is where brand safety will play its assigned role! Before understanding the importance, let us have a glimpse of what is brand safety? Introduction to brand safety! Brand safety measures are perfectly designed for protecting the online reputation of any brand and this will be done by restricting the brand from collaborating with any negative or inappropriate content. A famous quote says: “Brand safety is in the eye of the beholder—it all depends on what is or is not appropriate for the brand. For example, an R-rated action film might have different standards than a baby products company.” Now let us understand why we say that brand safety is a crucial aspect? Almost all the small and medium-size publishers are not having much knowledge about brand safety as a practice. And as a result, they definitely miss out on ad money monetisation prospects for them. Even the big and huge publishers such as news and magazine websites are trying hard to offer correct and pertinent information without making the use of any blacklisted or blocked keyword and losing out on any of these opportunities. Brand safety is a measure that will protect the publishers from any kind of risks also. Some of the potential risks included are illegally downloaded ads, fraudulent e-commerce ads, and invalid traffic. These might be exterminated with the publisher’s brand equity. Thus, there is a need to have a complete understanding of brand safety so that no publisher will avoid any kind of chance to leave money on their tables. What steps must be taken by the publishers? Have a check of your current position: Not just the use of blacklisted keywords, the advertisers should also consider their domain rights, a score of view-ability, fill rate, and historical bid price of the website you own. If in any of the cases, these numbers don’t match the standard of the advertiser, you can any moment be removed from the campaign of the advertiser. Thus, it is advisable for you to have a check on these numbers so that you avoid any penalty by the brand safety controllers. Do not try to touch the invalid traffic: When in contact with the non-human traffic, you might take down the brand safety score drastically. If by any chance any demand-side system catches a glimpse of any unusual traffic on your site they might mark your website as unsafe and even other buyers can’t see you then. Undeniably, this is true that the publishers can’t completely avoid the unusual traffic, in any if such case, they must be straightforward and the percentage of the invalid traffic should be shared in the policy of your website. Try to review the demand sources: It is suggested for you to choose the demand partners very carefully. Double ensure that they take and have adequate safety measures in the right place to avoid any wrongdoing like ad injection fraud, domain spoofing, and a lot more. Try to take some help from brand safety ventures: If you are constantly facing a declining fill rate and the reason is not known to you, then chances are the advertisers might have blacklisted or blocked you. There are innumerable reasons for this but don’t rule out the blacklisting at any cost. It is advisable to work directly with some of the prominent brand safety ventures or vendors. Along with the services for protections, they will also offer the perfect and required consultation to make a balance between genuine content and ad revenue. Have a clear understanding of brand safety: Brand safety is not only important for the advertisers but is also a vital concept for publishers of any size. Thus, it is well said that when you learn more about any problem, then you get closer to the solution. Marketers, as well as their businesses, had been somewhat ignorant about the occurrence of keyword blocking and brand safety. Any publisher will find it really embarrassing to get some screenshots from the advertisers related to their keyword usage on any disinformation websites and some extra hatred websites.

Marketing & Advertising has always been a field of jargon's and methodologies, it has been a market selected by a few and for long it has been able to keep it's dark secrets. As we have been moving towards a more digital world, the need for brand safety, trust, transparency and privacy are among the most discussed keywords among marketplaces that crowds marketers. We have tried summing the context of Brand Safety in today's digital world. 1. Ads served on unsafe environments An OTA (Online Travel Agency) player would never want a digital advertisement next to news page that's describing a recent travel accident or mishap or say an e-commerce player or any player would never want a digital advertisement next to page that describes a sensitive issue. These form of ad view-ability on unsafe environment doesn't just bring down the brand image as a whole, but also reflects upon lack of attention of marketers in house. We need to enable our programmatic technology to be able to predict ad placement by merely processing keywords on the page during ad placement, and prevent it from being served on unsafe environments. 2. Transparency of Supply Chain We have noticed an upward demand of marketers to be able look into their entire supply chain of channels their creatives run on, except the walled gardens such as Google and Facebook, marketers leverage on ad networks/agencies to exponentially scale their traffic, but often forget that non transparent supply chains target particular GAID's/IDFA of brand's consumers for their competitors. 3. Behaviour Bot Traffic and Trust One of the key reasons for lack of transparency in supply chains is interest of ad agencies to intentionally add bot traffic for financial gains. While traffic mixes have been a new age methodology for cyber criminals to minimise detection, unethical technology development to fudge the data with intermediary 3rd party tools and technologies. 4. Privacy, the new Law While the ad serving infrastructure is ready and deployed, and ad tech market is highly inflated and looms in darkness. A ever increasing demand of consumers demand has been constantly questioning the practices and ethics the market has been using for years. Today, consumers don't want to be tracked, they don't want to see ad's that are targeting on basis of their searches and research. While we are slowing improving the marketing and advertisement industry as a whole, bringing back the attention to the consumer needs and adding more value to the advertiser spends. The age of software infrastructure is over, marketers ain't looking for DMP's / DSP's /SSP's that can automate their workflows but they are constantly in search of algorithms and cognitive technology that can help them make more sense of their data assets. “People are spending too much time talking about ad-blocking and not enough time figuring out why people want to block ads.” Jim Stengel, former P&G CMO

If you are a bar owner and alcohol is in the news a lot, you might want your digital ad placed next to the news because it is getting a lot of traffic but you do not want your ads to be displayed next to a news article reporting a deadly accident involving drunk people or your ads popping up on a blog post which explains why you should cut down your drinking for a healthier body/mind. You might have an amazing scheme in head and on display that will drive up your sales but it will reflect poorly on your brand if it comes in the situations stated above. In this case, blacklisting some words/terms seems beneficial towards the image of the company. To understand this, let’s take another example, imagine you are a car dealership owner and you are promoting your new batch of fossil-fuel high- powered cars but your ad starts displaying on a website which is explaining how the population of polar bears and penguins among other Arctic and Antarctic animals has significantly plummeted because of increasing Green House Gasses (GHGs) and emissions. Most people will take that ad in a poor taste and question why people, industrialists in particular like you are being ignorant. It is often said that first impression is the last impression, if someone comes across your ad in this situation for the first time, they have full reason to believe that your brand is threatening the environment, even though it might not be completely true. But ads being displayed in the wrong parts of the internet is not the only brand safety concern for companies these days. With the internet progressing and bettering itself every day, the problems are getting more complex day-by-day too. Other potential risks to name a few involve invalid traffic, fraudulent e-commerce ads, domain spoofing, extremism, inappropriate content and illegal download ads. Advertisers should be vigilant of these tricks to avoid having monetary and reputational losses. Every product or service is designed keeping in mind a segment of people who are designated as the target audience for that service or product and if people from the targeted audience are not able to view the things on the market or if they are being seen by bots or people who accidentally clicked on the ads. It defeats the purpose of online advertising. Legitimate ad traffic is important for an online advertising campaign to be successful, only then does the brand truly realise its goal of serving the rightful customer. Otherwise, it is just efforts being wasted and so much time and energy spent in vain. Interaction with a genuinely interested user is how the brand can assure itself some growth, any other exchange will just be stagnating and not so useful. If we look at fraudulent e-commerce ads, it basically includes numerous tricks used by fraudsters to not only harm the safety of the brand (i.e. reputation) but also steal money. One common way is search ad fraud. In this type of fraud, expensive words are targeted by developing fake websites by fraudsters. Using bots, they drive up the clicks and then sell that ad space to unsuspecting advertisers. They bring in illegitimate clicks along with questionable content which may conflict with brand image and safety. Another, way of tarnishing a brand image/safety is an ad injection. Imagine you displayed you ad on a legitimate, well- known website. Since you thought that it is a safe website because it is reputable and popular, it does not mean that there is no scope for fraud there. Thing being said, it is safe to say that every website is vulnerable, some more than others. Now, these ad injections are ads slipped onto a website without permission of the publisher. This ad is just sneakily injected which has nothing to do with the website or your ad. But, it could be a bad look not only for your ad but also the website. The ad is generally pornographic content. In the examples above we have seen how a particular company/brand may be affected because of particular placement of ads in certain areas of the internet However, brand safety means much more than mere placement of ads on websites or a few random useless clicks, it is practically saving money which is lying on the table. It includes the image of a brand and what it associates with completely. It takes years to build a reputation and one wrong move can destroy it and cause monumental damages, especially in today’s world where we have the internet, though which information flows very quickly so does misinformation. Also, nothing ever truly goes away, in the sense that it is permanent, once information is on the internet, no Public Relations team can truly ever get rid of it, they can twist facts and present it another way but, never get rid of it.

If you have been working in the ad-tech space, you would often hear people talk about segmented user inventories through which companies in ad-tech space run digital campaigns. You can easily target customers by various segments i.e age, sex, income, geographic making reach of such inventories highly effective and revenue generating. Often such DMP's are build on years of work, partnerships and running 3rd party campaigns. The art of targeting users based in device ID, GAID's is become very popular among marketers providing ease and scale easily. But often, such device's ID's and GAID's become a problem for consumers as mobile applications which form the end point of publishers to gain new users, often get rigged and consumers are constantly monitored over new app installs, events on various app and even get monitored on call logs. These user inventories are more of inventories that have incent apps installs, which track these devices for any new activity, and in case of performance based activity, it often gets re-attributed by click injection feeding the top point as in an ad-network for conversions. Have you ever wondered how a click can be injected just before the install, all this is possible if the device is infected with a malicious app that is serving the purpose to fill clicks for organic traffic or hijack a paid marketing click and replace with a misattributed one. Data analytics have helped understand the anomalous behaviour, but is often reverse engineered from behind to evade fraud. Our patent pending algorithms are first of the kind, which detects changes in programmatic sequence making it a robust and reliable method to detect any kind of foul play.

Performance marketing has always been data driven, with vanity matrices guiding the performance KPI. It's been an exciting role for 21st century marketers to identify, target and retarget customers based on their actions and behaviour. Often this actions and behaviour of consumers is understood by data attribution. Each action of consumer creates a data footprint for him/er in the data attribution. This attribution is often used by marketers to define their KPI in performance campaigns. Last click attribution and multi click attribution has been current models of adoption for marketers define paying criteria for traffic providers. You would often find marketers running KPI's ranging from CPM to CPT, which is equivalent of feeding your entire sales funnel with some form of traffic. Why are these models proving to be insufficient to fight the menace of advertising fraud? Data attribution is often hijacked. How? Fake mobile apps or a publisher(app or web-based) working with 2 or more ad-houses or networks. Example : On my android device, suppose I have a Youtube app and a local news application, Now, if I saw an advertisement on youtube, and showed interest in the service being provided by the advertiser. The local news application, acting as a user tracking app, would inject a last click if I start using the service provided by the advertiser, claiming the traffic. In turn, making commission. While, the conversion really happened on Youtube. This form of ad fraud often called click injection is rampant among B2C advertisers, and often impacts minimum 20% spends on ad-networks. Why it is a problem? Hijacking user conversions for high value payouts in form of CPT campaign, has always been a playground of big time fraudsters. A network of these form of mobile apps are deployed intentionally. Who is hijacking and what is the source of hijacking is a mystery question for advertisers globally. How can Com Olho help? Com Olho neither uses rule based detection nor uses any form of blacklist to identify fraudulent traffic. We use sophisticated machine learning algorithms to segregate traffic based on degree of programmatic manipulation, which allows us to decide if a particular traffic provider has been hijacking traffic from other inventories i.e social networks. Want to know more? Request Demo Today.

Do we really need to keep on collecting all this "DATA" into sheets or tables or data-frames using misguided platforms? What would data collection lead to? Nested if-else's or Ctrl-Shift-L across the sheets and then dash-boarding? Few months back, we started building AI/ML based algorithms that could automatically make science out of the data created without a need of managed service or a platform provider that keeps on collating this data in a unified manner. Remember, collecting data isn't important, driving science out of it is, and keep on doing it for different data silos is required, that is what will unleash the power of super forecasting aka artificial intelligence. When it comes to advertising, banking, healthcare etc. Data means privacy, it means human's choices and believes, and in no circumstances we should be leading the industry into a perspective of collecting all these data silos under a single hood. Building this super forecasting capability to detect fraud in advertising industry was one of the first problems we chose to solve, I mean look at the data being generated in the industry, it's huge. Look at the below use-cases our algorithms have been intelligently learning on. Segregating Fake CPM traffic (Brand Campaign) This particular CPM campaign was being executed over a fake google play application which had an inventory of less than 5% humans. A systematic approach to create fake impressions was visualised using machine learning algorithm. 2. Segregating Fake CPI/CPA traffic (Performance Campaign) This particular bot operation is way to smart, it would mimic and execute human behaviour, falling inside the right ( time to install), create fake attribution events to evade fraud detection. Using state of the art ML algorithm, we were able to bust this operation which was costing advertiser over 15,000 USD for 100,000 performance installs/events. We are in constant quest to create solutions to the most pressing problems arising out to growing cyberspace. We need to constantly evaluate platforms not on the capability to collect and dash board large amount of data, but by their capability to build algorithms that can aid the building of true artificial intelligence. "An unsophisticated forecaster uses statistics as a drunken man uses lamp-posts - for support rather than for illumination. "

What is money laundering? The technique of transforming huge monetary gains from illicit activity into legal assets while hiding their real origins is known as money laundering. To combat such acts, governments all over the world have been increasingly tightening AML policies. Financial institutions are now obligated to adhere to strong anti-money laundering rules and to disclose any suspicions of money laundering activities. Money laundering has a significant societal impact since it fuels terrorism, trafficking, drug dealing, and other criminal activities. The problems and challenges with money laundering starts with: Rising AML operational costs: it is pushing financial institutions to seek alternatives to their present tools and technologies in order to avoid fines and penalties. The rise in false positives: it keeps compliance personnel distracted and as a result, resources are spread thin across all phases of the AML process. The prevalence of false negatives: sophisticated criminals who are able to circumvent AML protocols in order to perpetrate crimes. Difficulty is locating money laundering practices: Every year, money launderers become more skilled, establishing an elaborate network of identities and accounts through which to channel their illicit activities which makes locating the false negatives hidden deep inside the mountain of valid transactions very difficult and time-consuming. The graph approach A graph or network is a collection of nodes and connections (also called edges). Graph analytics is a collection of analytic tools that enable the investigation of links between items of interest such as companies, individuals, and transactions. It assists data and analytics executives in analysing linkages in data and reviewing data that is difficult to evaluate using standard analytics. In the field of anti-money laundering systems, the concept of networks and connection analysis is fundamental because it helps expose hidden aspects of transactions that are not discoverable by any other means. When paired with ML algorithms, these technologies have the potential to trawl through hundreds of data sources and documents, allowing financial institutions and AML specialists to quickly uncover hidden patterns and relationships in transactions. Graph analytics is essentially a set of analytic tools that allow you to "dig down" into complicated interrelationships between businesses, individuals, and transactions. For example, a major international investment bank in the United States is utilising sophisticated graph analytics to strengthen its fraud prevention activities, especially fraud detection for debit and credit cards. The organisation is integrating graph analytics into its machine learning system to discover data links between “known fraud” credit card applications and fresh ones. As a consequence, the bank can discover more suspicious trends, reveal fraud rings, and close down fraudulent cards more quickly. The bank will save millions of dollars each year as a result. Graphs may be used to detect anomalous patterns, which can aid in the prevention of fraudulent transactions. Terrorist activity has been found in certain cases by examining the flow of money across interconnected banking networks. Fig 1 : Fraud detection with regular analytics and with advanced graph analytics can be visualised from Fig. 1. The use of graph analytics allows for the dynamic study of relationships within a huge dataset. It is possible to investigate and visualised who and what a client is linked to using data as diverse as an email, a phone number, a device, transactions, and so on. The detection of accomplices becomes very rapid A regular fraud detection case A tip or a detection system may occasionally flag a client or a transaction as suspicious. In this circumstance, it is vital to determine whether or not this particular questionable circumstance is isolated. The customer might be a member of a larger criminal ring, or the transaction might be part of a broader operation. In the absence of more information, it is critical to pursue as many leads as possible. This necessitates investigating what the customer or transaction are related to. Consider a simple payment made using a digital payment provider such as PayTM, PayPal, Google Pay, Amazon Pay, or Razor Pay to see an example of possible fraud and why it is so hard to identify using standard analytics. A user has opened a new account that is connected to their Bank X credit card. They have connected their phone number and email address to their account as part of the setup and two-factor authentication. The user uses an Apple iPhone X with the registered phone number as their device and starts a payment of Rs. 5000 to another account. Because the user is a new user with a new phone number and email address, there are no red lights or alerts in a standard financial services fraud detection solution at this stage (none of these have been associated with any fraudulent transactions in past). Regular analytics does not uncover anything strange or suspect and the payment passes through without being reported or refused. Use of Graph analytics on the case Deeper analysis with a native parallel graph analytics technology, on the other hand, offers a different image. There is a fraudulent activity related with a gadget, phone number, and stolen credit card six levels inside. Here's how it goes down: The payment's recipient account belongs to a user who authenticated the account with a Phone Number as part of the account registration procedure, and that phone number is used with a different device Apple iPhone Y. As the deep link analysis searches the history of previous fraudulent transactions for devices linked with those transactions, it discovers that this Device was used last year with a different Phone Number to set up a separate Account. This account initiated a payment that was subsequently discovered to be fraudulent since it was paid using a stolen credit card. Advanced analytics using graph analytics may go deep into the related data, in this case six links deeper, to uncover the link to earlier fraud in real time, and the payment transaction is refused as a consequence. As you can see, advanced graph analytics is required for real-time payment fraud detection — and this analytics identifies fraud three layers "deeper" than normal analytics. This disparity between normal and advanced graph analytics can result in hundreds of millions of dollars in fraud losses. Advanced graph analytics with real-time processing can process the payment transaction in under a second and then perform the multi-connections query on the related dataset. In other words, the system must check every connection along the path from the person initiating the payment to the ultimate receiver, the one involved in fraudulent Payment. Clearly, fraud detection is at the top of every financial services organisation's priority list – and this is unlikely to change. As fraudsters grow increasingly tech-savvy, it is critical for businesses to keep one step ahead of them. These deeper insights are enabled by advanced graph analytics, which complements conventional BI technologies and powers AI and machine learning. Hence, as a consequence, firms can anticipate and avoid possible fraud while also safeguarding their consumers.

Advertising Fraud is a rampant problem. Fraudsters, from faking user behaviours or stealing Organic/Google/Facebook converted users and tagging it as their own using programmatic mis-sequences is become a common problem for performance marketers. People or Group of people committing to advertising fraud in their head feel that they have been completely able to shadow the unethical practices, and have successfully created what we call a black box model. Today, even the most strict KPI's ain't safe, with advertiser paying almost twice for the acquisition of the same customer. Block-chained user flow, which is programmatically hashing the last attribute to the the next attribute. Any addition, deletion to the flow, can automatically raise alarms of suspicion. Market dominating leaders have already tested flows of block-chaining the user journeys, but are highly unsure on industry vide adaptability and success rates on B2B partnerships. While it seems promising for Google or Facebook to test these new capabilities, it becomes imperative for fraudsters to understand the hashing criteria to reverse engineer and show conversion theft legibly. Why networks/affiliates want to move in adopting blockchain? 1. Lesser transparency to the advertiser. The advertiser will be made to believe that their technology is cryptic, which is using blockchain, which helps build the trust back which networks and affiliates have lost over last 5 years because of advertising fraud. 2. They want to move away from the big data attribution. Over last 5 years, big data has provided advertisers not just understand how brand consumers behave but also how to create personalised experiences. On the other hand, big data has been a huge game changer for fraud detection. Majority of advertisers today deploy either in house or an outsourced 3rd party effort to keep a watch on fraud spends. This isn't good for networks or affiliates, as it makes their unethical game out in public. 3. Blockchain will give more darkness to the advertising fraud prevalent in the market, as it would become more decentralised and behave like a black box model. With technology, data science and machine learning creating a bigger view for marketers, now CXO's are looking for tools that can enhance their view further to make much bigger and better decisions.

Com Olho which in 2020 became the first company in India to be granted a patent for non-rule based mobile ad fraud detection and prevention has created a single bullet solution for fighting the menace of affiliate fraud and content piracy. While working with top advertisers in India, the team realised the need for tech based digital governance to fight affiliate fraud. In India today, a lot of vendors are deploying rule based methodologies to fight the menace of affiliate fraud i.e VPN detection, disposable email and phone numbers detection, fake data fills and device farms detection. This methodology of detecting affiliate fraud is old school and leads to more affiliate fraud than it was at the first place. Addressing these problems, Com Olho has come up with proprietary system that leverages military grade encryption and serves it using real time API which has been a core research focus of the company over last few quarters. The company has beta-tested the product already and looking to bring this to market by end of this year. Affiliate fraud is not only impacting advertisers, but is also impacting consumers by stealing away sensitive data information. Over the last fews weeks of testing the technology, Com Olho has been able to detect tag-based affiliate fraud impacting leading e-commerce companies, financial institutions etc and government ministries. We have also seen a large amount of pirated content in circulation stolen from all the famous OTT players in India. Radhe, a movie recently released digitally under SKF banner has been compromised because of this menace, which has led to huge losses to the content makers. Using trademarked brand names, the fraudsters are aiming to spread mis-information, fake news and also earn advertising dollars through affiliates and google display network. Founder & CTO at Com Olho, Abhinav Bangia says, even with anti-ad fraud vendors in India, the problem remains unchecked for a simple reason, ineffective non-tech solutions. Instead of delivering tech-enabled solutions, vendors are focusing on creating blacklists and involving huge human bias for detection of ad fraud and content piracy. We are in final stages of filling the patent, and hopefully would address this unchecked problem plaguing our advertisers and content markers budgets and reputation.

If you have been in the ad tech industry, you would often hear people say "Fraudsters are always a step ahead in the industry". Can we conclude if the fraudster in the industry is some of the ad-tech vendors itself? Consider solving the case below by following the conversation. Case : Three people form the advertising industry. The advertisers always tell the truth. The ad-tech vendor never tell the truth. The fraud detection vendor alternatively tells truth and lie. The world's renowned explorer questioned all of them. The advertiser, ad-tech vendor and the fraud detection vendor. Let the 3 people be Jack, John and James not in the same order. Explorer : Jack, which section of industry do you belong to? Jack : I'm an Advertiser. Explorer : John, to which section do you belong to? John : I'm an Ad-Tech Vendor. Explorer : Was Jack telling the truth? John : Yes. Explorer : James, to which section do you belong to? James : I'm an Advertiser Explorer : To which section does Jack belong to? James : Jack is a Fraud Detection Vendor. Now that you have heard the conversation above, can you tell which person is the Ad-Tech vendor? A) Jack B) John c) James Once you arrive to the answer, you would be able to get a bitter insight of the advertising industry today. Below find an interesting structure of Ad-Tech ecosystem, understand the bridging layers here that lead to fraud. 1. A layer of semi owned dummy mobile apps are needed to steal data inventory from real publishers, fraudulent publishers and ad networks. This allows the ad tech company to mix traffic, shadow the original traffic sources not allowing transparency across supply chain. 2. A layer of semi owned dummy mobile apps are needed to simulate human behavior through bots after reverse engineering fraud suites which are build internally or externally. We at AdIQ, are building an encrypted inventory matching technology which would help us expose this layer of dummy apps with legal addresses that ad tech vendors use in the industry to gain inventory and game the system now and then. We are also studying what personality traits make up these cyber criminals in the market and how they been in the industry for over the decade. "What is clear is that we need to cut through the chaos and focus what is best for our advertisers, not anything else."

Given all the developments in digital advertising over the last year, with Apple and Google announcing major platform changes, staying vigilant against mobile ad fraud may have taken a back seat. Mobile ad fraud, however, is never going away. Mobile Ad Fraud is a subset of ad fraud plaguing mobile based performance campaigns. It is a collective term used to describe a combination of tactics used to stop digital ads from being delivered to the audiences or spaces for which they were intended. These tactics often include the use of bots, click injection, click spamming, organic hijacking, device farms, SDK spoofing etc. These tactics allow ad fraudsters to syphon off enterprise’s ad spend dollars while the ads themselves fail to generate brand exposure, leads and sales because they were never seen by an actual person. Besides hurting the advertisers, mobile ad fraud also hurts publishers by driving down the ROAS and decreasing the overall value of ads. Advertisement-related frauds will continue to be a major threat in mobile environments in 2022. Last year broke records for ad spending. According to a recent forecast, global digital advertising in 2021 was expected to grow by 15.6% over 2020, reaching $705 billion — well above pre-pandemic levels. Unfortunately, this advertising boom also triggered mobile advertising fraud. As businesses kick off their 2022 marketing plans, there's one thing they shouldn't overlook — a strategy for combating mobile ad fraud to protect their return on ad spend (ROAS). According to a study, ad fraud costs the marketing industry an estimated $51 million per day, and these losses are likely to increase to $100 billion annually by 2023. Sophisticated nonhuman bots, which are actively involved in ad fraud, are responsible for roughly 18% of all internet traffic in the marketing business. Digital advertising operates within a complex system with many loopholes where fraud can infiltrate, but with a little guidance, advertisers can mitigate fraud that gets past prevention measures. Approaches To Fighting Mobile Ad Fraud Fraud is a moving and changing target, and it hides behind the performance numbers CMOs are looking for in the first place. The goal is to not wait until something major occurs to make you pay attention. The goal is to pay attention on saving ad spend that doesn’t take much time or resources from enterprise's marketing team. Look Beyond Install Numbers Examine your conversions, post-install rates and numbers using cohort. This is where you're more likely to notice anomalies like a strange device ID or email address, and then check into the behaviours linked with these potentially fraudulent identities. Click to install time is another metric to consider. Instal events that happen too quickly or in groups can be marked for further evaluation. Be Aware Of Your Audience Reach User acquisition is one of the most important aspects, however the farther your reach, the less trustworthy the traffic becomes. You'll be more exposed to fraud if you use lower CPIs or have a wider reach. There are only so many ad partners out there, therefore in order to meet demand and for client's growth, they may have to rely on marginal traffic. To stop suspicious traffic, make sure you have traffic verification methods in place. Mobile Ad Fraud Detection Some enterprises use mobile ad fraud detection software to spot invalid clicks across their programmatic/display advertising, as well as paid search and social channels. The software can detect clicks and impressions that are generated by bots on paid campaigns and then blocks them, thus preventing them from continuing to syphon money away from the campaign. Mobile ad fraud detection software relies on detecting patterns that resemble suspicious actions in an ad’s impressions, clicks, traffic or IP addresses — or a mix of all those data sources. It compares clicks on ads to its database, and if it detects an anomaly, it notifies users in real time so advertisers can analyse their data. Data Analytics Data analytics helps enterprises pinpoint sources of fraud. Advertisers can use data analytics tools to get a variety of performance indicators on their marketing efforts, such as web traffic across their digital assets and information on potential customers who interact with their ads. With this much bad data in enterprises marketing stacks, it’s no surprise that a significant portion of their total ad spend doesn't deliver any return on investment. Modern data analytics tools utilise machine learning to analyse massive amounts of data and discover anomalies that often indicate fraudulent activity. In turn, this helps enable advertisers to identify fraudulent traffic and prevent further damage by quickly adjusting their ad strategy and divesting from bad traffic in favour of good traffic. Verifying their data across multiple channels also helps enterprises prevent bad data from impacting the rest of their data set, ensuring that their ads reach real and actual target customers. The Takeaway Mobile ad fraud has been a major issue worldwide in the last two years, fuelled by rise in digital during the pandemic and it's projected to become an even more concerning issue in 2022. To get the most from their campaigns, enterprises will want to consider investing in solutions that validate the accuracy of their data and ensure that their ad budgets result in impressions and clicks that actually generate the desired results and values.