Article | February 10, 2020
Small businesses in emerging economies are notoriously underfinanced. Despite making up over 99.5% of the economy in Latin America, SMEs face a financing gap in the trillions of dollars. For example, up to 78% of small businesses in Argentina and 45% in Peru struggle to grow because of financial constraints. Numerous articles and institutional white papers point to the lack of trust between banks and SMEs as a major cause of this financing gap. Banks simply do not know how to accurately calculate small business risk, especially in volatile economies in Latin America, so they offer high interest rates or pass on providing credit altogether.
Article | February 10, 2020
Customers in the financial services industry want personalized experiences. They, in fact, expect and demand them from their service providers. They prefer to stay loyal to a company as long as they receive this special treatment. As a result, personalization has become the number one priority for marketers in the industry today. They are waking up to the realization that delivering personalized experiences highly depend on understanding customer data.marke
Very few companies have the means to understand this data and use it to enrich the customer experience. The technology that has been recently making waves in every industry is known as the Customer Data Platform (CDP). A CDP is a packaged SaaS (software-as-a-service) product that is designed to build a unified customer database for an organization. Implementing a CDP can help achieve consistent customer engagement, increased loyalty, and higher sales.
David Raab, CDP evangelist and Founder of the CDP Institute, was invited as a chief guest at the Customer Data Summit 2018 event organized by Lemnisk.
David is a widely recognized thought leader in marketing technology and analytics. He was one of the first people to recognize that digital marketing systems were not just proliferating but also the data that these systems were throwing up were getting grouped into silos, making it really hard for marketers to understand customers holistically.
David also realized that there was a tremendous opportunity if he could bring these disparate systems together. Around this insight, David coined the term CDP and founded his institute in 2016. The CDP Institute’s work has been seminal in helping marketers understand the need for a CDP and the ways that they can derive value from it.
David’s thought-leadership session imparted the following key insights:
The most challenging barrier to Marketing Automation success is data integration between the various marketing systems of an organization. Financial marketers in Asia face the same challenges as their peers elsewhere, which include unifying customer data, providing superior customer experience, working within compliance constraints, and finding the budget to pay for solutions.
The CDP industry has seen a good growth rate of around 73% over the last 12 months. Two-thirds of the growth is attributed to new vendors and the remaining to existing vendors. The adoption rate has been high for B2C marketers as their businesses depend highly on user engagement and digital conversions. Companies that opt for a CDP prefer to have a complete packaged solution that includes the core CDP functionality along with analytics and engagement.
A CDP works well when all marketing systems are interconnected. One interesting observation is that one-third of CDP users lack an integrated technology stack. Companies that claim to have a CDP do not have this system integration and, therefore, do not fall under the CDP-classified vendors.
Things such as churn prediction and predictive modeling are a set of classic algorithms that thrive on good data. Artificial Intelligence (AI) is totally data-driven and works well with data that is highly detailed. A CDP can play a major role in developing custom algorithms and advanced intelligent systems such as AI. One of the things that it can do is create a standardized variable or model score and make that shareable to all systems that it connects to.
Of its various capabilities, a CDP also enables cross-device personalization by associating each device with the customer’s master ID when they log in. The master ID is used to build a unified customer profile with all device data. The right message for each master ID is selected and shared with all devices. The unified and complete customer profiles help financial marketers in selecting the right message and deliver a consistent experience across all devices.
It is still early days for a CDP in Asia. Many organizations are still at the stage of learning for themselves why options such as DMP (Data Management Platforms), Enterprise Data Warehouses, and marketing clouds won’t solve the problem that a CDP addresses. The core technologies used in Asian financial institutions can support any level of marketing sophistication that their users are ready to deploy. The early CDP adopters are touted to have an advantage over others in the industry.
Article | February 10, 2020
Going far away beyond conventional attack detection, advanced machine learning operations assist organizations to stay one step ahead of financial fraudsters.
We hear tons of stories about account takeovers and hacking also. How can financial institutions detect and mitigate these attacks?
The world of fraud prevention in banking institutions has always been supported by rules. Bankers and their engineers were uniting rules engines on the banking data system to stop or identify common fraud patterns. For quite a while, this was sufficient. But today we are experiencing a change of society, a digital and technological revolution. Following the primary iPhone, and therefore the later mobile internet explosion, people are interconnected all the time, everywhere and for all quite useful. In this digital age, the digitization of means and behaviors forces corporations to revise their business model. As a result, banking institutions are going massively online and digital-first. Both the bank users and customers have unfolded their behaviors with the brand-new means offered by the digital era.
Learn more: https://deck7.io/Women-Leadership-verrency-audrey-blackmon
With the shift towards universal digitalization, perpetrators are finding new weak spots in financial digital applications. Ironically, the technology works both ways: it accommodates firms to supply more reliable customer experience and optimize operations and, at an equivalent time, aids cybercriminals in performing numerous sophisticated unlawful schemes.
According to the Association of Certified Fraud Examiners (ACFE), 30pc of fraud occurrences happened in small businesses, and 60pc of small-business fraud victims did not retrieve any of their losses.
According to Statista, in 2017, the global FDP (fraud detection and prevention) market was calculated to be worth $16.6 billion.
According to McKinsey, worldwide losses from card fraud could be close to $44 billion by 2025.
Financial crimes do not limit crimes like credit card fraud, tax dodging, and elder abuse. In fact, it includes much broader offenses – such as Identity theft, human trafficking, phishing, pharming, drug trafficking, money laundering, and terrorist financing that can have enduring impacts on society.
Fighting financial fraud is difficult because fraudsters frequently change and adapt. The moment you figure out how to identify and prevent one scam, a unique one emerges to take its spot. Identifying, eliminating, and blocking these threats are sensitive points for e-commerce and banking industries. Sincerely, the best technology for combating fraud is one that can evolve and adapt as instantly as the fraudster’s tactics. That’s what makes machine learning (ML) systems ideal to fight fraud and financial crime.
The big problem is that companies think they need to establish rules, policies, and procedures to prevent fraud. But today’s criminals are much more sophisticated and are able to circumvent these business rules. Businesses need to take a more dynamic approach that includes business rules as well as machine learning and AI to learn from evolving criminal behavior and deliver a more sophisticated and effective approach to dealing with financial crimes.
Andrew Simpson, Chief Operating Officer of CaseWare Analytics.
Why use machine learning to combat financial fraud?
Machine Learning knocks down the conventional ways of detecting fraud. It’s quicker, works with extensive amounts of data, and doesn’t rely on human resources. When designed optimally, it absorbs, adapts, and uncovers emerging patterns without the over-adaptation resulting in too many false positives. It’s time for ML to conclusively take center stage in assisting firms to recognize and counter fraud as fast as it’s performed.
How Machine Learning Helps in Fighting Fraud and Financial Crime?
Machine learning can learn normal behavior from training data and recognize abnormal behaviors that indicates money laundering, like, when money is transferred between suspicious geographies, active movement of funds between different accounts, or invoicing number sequences have been falsified. Machine learning is continually learning, and so they can recognize when the pattern of laundering change and adjust rapidly.
Analyzing Huge Amounts of Transaction Data
One of the most powerful features of machine learning algorithms is that they can analyze huge numbers of transaction data and flag suspicious transactions with highly accurate risk scores in real-time. Its algorithms serve 24/7 and process an immense amount of information with the flip of a switch. This risky analytics method recognizes complex patterns that are challenging for analysts to identify; this means banks and financial organizations are far more operationally proficient while detecting more fraud.
The algorithms take various factors into account, including; customer’s location, the device used, and other circumstantial data points to form a detailed picture of every transaction. This strategy improves real-time decisions and protects customers against fraud, all without affecting the user experience.
Thanks to extensive technological development, organizations will frequently rely on machine learning algorithms to determine which transactions are suspicious.
Learn more: https://www.sas.com/en_in/insights/articles/risk-fraud/strategies-fraud-detection.html#/
Supervised and Unsupervised Learning for Detecting Complex Patterns
Machines can be programmed to self-learn in an unsupervised model with ML so that transactions that do not conform to a set pattern are recognized and hence can be actioned upon in right period.
Machine Learning automatizes the extraction of aware and unaware patterns from data. Once it identifies those patterns, it can employ what it learns to new and unseen data. The machine learns and modifies as new outcomes and new patterns are introduced to it via a feedback loop.
In fraud detection, supervised machine learning algorithms can self-learn from targets within the data. While training a supervised model, it's important to present to it both fraudulent and non-fraudulent records that have been labeled as such.
Unsupervised Machine Learning is different. It reveals potentially unusual risks you might not watch for because it works without a target. Instead, it looks for irregularities in the data.
Machine Learning in Fraud Detection
The fraud detection method employing machine learning starts with gathering and segmenting the data. Alongside this, the machine learning model receives training sets that train it to predict the possibility of fraud. Conclusively, it creates a fraud detection model:
Input data- The first step is data input, which differs in Machine learning and humans. Humans strive to comprehend massive amounts of data, such a task is a five-finger play for ML. The more data an ML model eats, the better it can learn and polish its fraud detection abilities.
Extract Features- Extracted features defining good customer behavior and deceitful behavior are added. These features normally include the customer’s location, identity, orders, network, and preferred payment method. Based on the complexity of the fraud detection system, the list of examined features can vary.
Train Algorithm- Further in this process, a training algorithm is launched. In short, this algorithm is a collection of rules that a machine learning model has to pursue when deciding whether an operation is genuine or fraudulent. The more data a business can supply for a training set, the more reliable the ML model will be.
Create Model- After the training is over, an organization receives a fraud detection model acceptable for their business. This model can detect fraud in no time with great accuracy. To be efficient in credit card fraud detection, a machine learning model needs to be continually improved and updated. Eventually, fraudsters will turn up with new bamboozle to game the system unless you keep it updated.
Employing advanced fraud protection and detection systems electrified by ML, multiple industries can keep their finances secured.
Capgemini alleges their ML fraud detection system can lessen fraud investigation time by 70% while boosting accuracy by 90%.
Another ML fraud prevention solution provider, Feedzai, alleges that a well-trained machine learning solution can recognize and prevent 95% of all fraud while reducing the amount of human labor needed during the investigation stage.
Reduction of False Positives
With the level of complicatedness in today’s financial infrastructures, the term ‘false positive’ has become nearly correlated with the industry’s efforts to fight fraud. One of the banking’s most significant challenges is to minimize the number of false positives being generated, thereby saving time, money, and bypassing needlessly frustrating customers.
AI and machine learning play a significant role in this area. Because they are proficient in examining a much more comprehensive set of data points, connections between entities and fraud patterns – including fraud scenarios not yet known to fraud analysts – the predominance of false positives can be extremely reduced.
Bringing it all Together
Multinationals like Airbnb, Yelp, and Jet.com are already employing AI solutions to get insights from big data and counter issues such as fake accounts, account takeover, payment fraud, and promotion abuse. Machine learning entertains all the messy work of data analysis and predictive analytics and empowers companies to grow and develop secure from financial fraud and crime.
As mentioned, machine learning can be very convenient when it comes to fighting cybercrimes. ML prevents critical attacks on users’ and companies’ finances. It’s a quick, up-to-date, and cost-effective method to shield customers and the company’s data.
Article | February 10, 2020
You heard that right.
It’s not “if” PayPal will replace cash — but “when”. But, where’s this coming from?
Well, we already know 73% spend money via apps.
So, it was time to speak with 200 PayPal and Venmo consumers to understand their shift to mobile app payments. After watching them leave these apps, we sent a survey. And, I can tell you…they think cash is a thing of the past.
Here are 3 reasons why.
Reason #1: It’s familiar.
Branding is the foundation of familiarity.
And PayPal is comin’ in hot — even if 6% of this group isn’t familiar with PayPal. Now, that’s interesting, huh? It may be that Venmo users aren’t aware PayPal owns them.
Either way, it’s not stopping PayPal.
Think about Bandaid and Kleenex for a sec. When was the last time you asked for a bandage or tissue? Exactly. Their brands are so strong, they’re actually nouns now.
If PayPal can reach that level…cash has no chance.
They’ve already got a following…Right now, 66% of the people we spoke to prefer their mobile app payment to cash or credit card. This isn’t a phase either, 8 in 10 people have used a mobile payment app for at least two years.
So, it’s not a trend.
Electronic payment apps are very top of mind for these users. In fact, 43% use a mobile payment app weekly. They’re in such high demand, 34% say it’s very important to have multiple payment apps – an option for every scenario.
Payment at the push of a button.
Speed at its finest.
Reason #2: It’s instant.
This isn’t business, it’s personal.
You love instant gratification, so do I. And everyone loves speed when it comes to money. So, with 94% of the people we spoke to on PayPal for personal use, instant access comes in handy — 49% are here to pay back friends.
Here’s what else they’re doing…
Why are there so many use cases?
Well, it’s simple.
Yes, 72% use PayPal because it’s easy. Think of it this way. You spy a beautiful red sweater online, a must-have. You’re going to buy it. But first, you have to make a choice. You can walk to your purse… and fumble through gum, cough drops, and kid toys until you find a credit card…
Or, you can just log into PayPal.
Maybe that’s why 43% of PayPal users hand over the app at grocery stores, and 42% use it at restaurants and retailers. With more places adding app payments each day, it’s easy to see why the group we surveyed loves it. Already, 52% use it in lieu of cash or credit.
Imagine what will happen when all merchants accept it…
We’ll see the death of cash and credit.
Reason #3: It’s secure.
73% of PayPal users say it’s their go-to payment app. They’re relying on the safety of the app and that’s important.
Why? Well, because 15% choose to use a mobile app payment for its security. If you’ve ever lost a lot of cash or had your credit card stolen, you know how valuable that is.
Peace of mind.
Not only are mobile payment apps familiar, easy and instant — 37% use them because they’re secure. At this point, you may wonder, “if everything is so positive, why hasn’t PayPal already replaced cash?”
Those we talked to, who don’t use PayPal — or other app payment options — as often, can’t because it’s not as widespread. Yet. Give it time and you’ll soon see buyers getting rid of credit cards and killing cash.
It’s a matter of time.
This is what your buyers want.
61% of PayPal and Venmo users believe mobile payment apps will soon replace cash/credit cards. Now is the time to prepare, as cash may not last another 5 years.1 Do the work now, so you’re ahead of the game.
This is market research. You can actually survey the same group of people we spoke to here. Or, grab a whole new group of consumers to dive in deeper, based on the app behaviors you need to understand.
Stay on top. Use consumer behavior to stay a step ahead of your competition.