How to Fight Fraud and Financial Crime Using Machine Learning Applications

RASHMI SINGH | April 20, 2020

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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.

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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.

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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 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.


Westlake Financial Services

People. Purpose. Passion. These are the hallmarks of Westlake Financial Services, and have been since our inception in 1978. Westlake has over twenty five years experience in sub-prime automotive financing, helping car dealerships do what they do best: sell cars. Our unique credit approval software (known as "The Buy Program ©"​) allows dealers to get an on-the-spot loan approval with out waiting for callbacks, fax-backs, or rehashes.


High Frequency Traders: Masters of Modern Financial Markets

Article | November 16, 2021

High Frequency Traders (HFT) play a ‘need for speed’ game day after day in a quest to capture smaller and smaller profits. To be a successful high frequency trader there are two mountains to summit. The first, is the development of a system which can analyse data in as close to real time as possible. Here, this would involve not only monitoring prices, volatility, clustering; on multiple exchanges simultaneously. There is also the need to monitor slower items such as fundamentals, news streams and annual dividend pay-outs. It is likely that many high frequency traders are using artificial intelligence to do this, as they must process big data sets in as close to real time as possible. Thus, this need for speed has started an ‘arms race’ between traders to possess the fastest system in the market. This links to the second of the two challenges: the minimisation of latency, or in plainer terms; the maximisation of speed. Information must be processed as quickly as possible, a trading strategy devised and a market order submitted. Ideally, quicker than any other trader. To be clear, there is always a first movers’ advantage when trading information into price! To give a sense of this speed, consider that all of the above can be achieved in less than the time required for a human being to blink. This technology originates in the digitisation of exchanges, for example the NASDAQ exchange opened in 1971 as the first electronic exchange. Later in the 1980’s, proprietary information services became available. The most well-known of these became Bloomberg which began in 1981. The faster trading conditions and greater availability of information has opened up the gap between human traders and algorithmic traders, which has become wider to a point where their ability to interact with each other is questionable. Estimates vary; however, one reliable source suggest that 73% of the US equity trading is now undertaken by HFT’s . In my own research in ForEx markets for highly traded pairs, the HFT incidence can be up to 8%, even in an over-the-counter market. To illustrate an issue HFT activity creates, consider that a high frequency trader can observe information, identify which asset is involved and the new ‘correct’ price, and submit an order; all in the time needed for a human trader to blink. This mismatch is especially problematic where liquidity is provided to markets by high frequency traders. Here, it is very difficult for slower human traders to access liquidity as it does not remain in the market long enough as HFT matches against HFT. In their defence, a high frequency trader would tell you that they provide liquidity for rare assets and also work to keep bid ask spreads tight. Also, it is not debateable that HFT does make a significant contribution to keeping prices correct, in terms of reflecting all known information and holding the law of one price across trading venues. So, it can not be as simple as concluding that HFT is all bad and progress should be revered. At this point we need to know that a HFT is typically an inventory neutral trader who will trade to capture spreads. Typically, individual trades earn very little return. Where the spread capture takes place over short time periods; this is known as a ‘scalping’ strategy. A trader who can work at a sufficiently low latency, will be able to run a scalping strategy over six ticks of the market which in some cases, can occur in less than one second. High Frequency Traders in their efforts to capture spreads could also increase price volatility. This may especially be the case where a trader, is placing a large order which incentivises HF traders to try and provide liquidity whilst capturing a spread. As this increases the price impact of the large order, there is an incentive to find a way to avoid the HF trader’s attentions. Such places exist away from the regulated exchanges; these off exchange liquidity sources are known in the trade as ‘dark pools’. These remain legal for trades above a certain size. A more pertinent issue in terms of the fragmentation of markets, is the creation of trading venues which seek to protect slower traders from predatory high frequency traders. Many regulatory tools have been proposed to constrain HFT activity. National regulators have over the last decade developed governance standards for algorithms used in trading, to incentivise developers to have full awareness of the behaviours of their creations under the full range of market conditions. On balance, I am not in favour of solutions involving lags or generally slowing down trading, as the race to the front of the queue still exists. Rather, any intervention needs to alter the desire/incentive for some activity to take place. For this reason, minimum order resting times, perhaps as little as one whole second would remove the incentive for aggressive scalping strategies.

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How to Tap the Power of Strategic Alliances and Partnerships?

Article | November 2, 2021

Why form strategic alliances and partnerships? Strategic alliances are key to business growth. They bring our strengths together to serve our clients better. I believe that each partner offers unique benefits to tap into and help achieve value at scale that the digital world demands. The North Star that guides our partnerships is our purpose to engineer sustainable businesses to improve everyday life. The partner ecosystem strives to bring end-to-end solutions to our clients and to continually infuse innovation as well as best-of-breed thinking to influence business outcomes. To that end, we constantly explore the potential of emerging technology, growing our relationships and building our partner network so that we bring fresh ideas and custom-made solutions to better serve our customers. How to establish winning alliances? The heart of building and nurturing alliances lies in designing and managing them to foster collaborative behavior. It also requires being aware as well as being able to mitigate the factors that can result in alliance failures. A successful partnership is pivoted on the winning formula of, “1+1 is greater than 2”. We work with our strategic alliances, the recognized leaders as well as the disruptors in the industry to generate Return on Investment (RoI). Best practices to develop and nurture a differentiated alliance: 1. Laser focus on who and how to partner: While it may look good to have a vast number of partners tagged to one’s brand, it is often an ineffective approach. It simply spreads the focus too thin. I believe in working with a fewer number of strategic partners, understand their operating model, craft a solid go to market strategy and implement it with the endorsement from executive sponsors. This means, aligning on a singular vision – agree on the impact we want to co-create and then formalize an execution plan to co-sell. It is equally important to highlight the strength of alliance to all the stakeholders across the business. While the fundamentals of how to run an alliance remain the same, each vendor is unique: they vary by industry, region, size, complexity, and hence require a bespoke approach. Strategic alliances are not a one-off transactional relationship, but a long-term view of how the partners can generate synergies to help address clients’ pain points. An added dimension to achieving partner success is the “three way” or “triple play” partnerships. An alliance of three partners with complimentary strengths and assets can yield competitive advantage. 2. Leverage the power of networks:An alliance success is often determined by the power of networks. Trust is the heart of any network. A well-run partner ecosystem relies heavily on the people driving it. Professional networks in the industry across geographies and executive connect at both business and technology level are pivotal to run a well-oiled partnership engine. I have observed that successful alliance leaders not only cultivate relationships across functional areas internally, but also within the alliance organizations across the regions and with the clients and prospects. Even if one’s purview as an alliance leader is a certain partner (industry and geography), it is important to be able to deal with the cultural differences. This is true especially when engaging with the stakeholders across the organizations and regions. It is getting even more relevant in today’s hybrid mode of work. 3. Partner Up: A consistent communication with our partners helps us uncover potential opportunities early in the sales cycle and iron out any differences. Regular partner governance ensures legal compliance and privacy imperatives are embedded from the beginning. This allows us to not only jointly solve the challenges our clients grapple with but also to co-innovate. Cadence also helps organizations to predict demand, timely invest in training, certifications and overall enablement of associates to be delivery ready. After winning business together, partners should look to leverage these successes with the support of marketing. Partner marketing can elevate and amplify the visibility of alliance across social channels. The impact can be seen in terms of accelerated lead volume, higher deal velocity and expanded deal size. Simply put, it improves the overall health of the pipeline. An ideal alliance leader: Gone are the days when alliance management was a nascent business function and an after-thought. Today, as forward-looking businesses are leveraging their partners to tap into newer revenue streams, the art of managing alliances is now being recognized as a business acumen. Organizations are investing in these roles and are assigning Key Performance Indicators (KPIs) to generate partner-led revenue. Increasingly, alliance leaders directly report to the C-suite. Another clear trend is, often professionals who have spent a certain time in the industry and have taken up a variety of roles across geographies are hand-picked to don the mantle of an alliance leader. This can work well given that these individuals bring with them a well-rounded perspective of the industry and a vast professional network. This comes handy to diligently navigate both internal and partner organizations, align and advance. Better together: Businesses can radically improve their alliance success rates by incorporating the best practices. They need to invest in people, processes and relevant technologies to derive the full potential and future proof their alliances. It is both an art and a science. Therefore, it is important to build an alliance team, which is both diverse and inclusive. Such a team sparks new questions, challenges the status quo and fuels outperformance. The rewards of adopting alliance best practices can be big. The risks of not doing so may be even bigger.

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Trends Banks Can’t Ignore in 2022

Article | October 29, 2021

The competitive landscape of banking is changing rapidly. The non-traditional upstarts are threatening to disrupt the existing century-old business model in the financial industry. Banks have never experienced change, as we’ve seen in the last couple of years. Customers prefer Smart innovations like cryptocurrencies, peer-to-peer lending, smart contracts, new types of online security, and more. Some banks are adapting to meet these new challenges, while others are sticking to their old ways. The question is will they be successful, or will they be left out? And that is exactly what we would like to figure out with an in-depth and data-backed analysis. Based on this analysis we have answered the big Q in the industry- What do banks need to do this year to prepare for 2022 and beyond? Here is our take on trends banks can’t ignore in 2022. 1 Social Media Storytelling and Copywriting Contrary to popular belief, the real story of today’s customers is not all about you, your brand essence, or your products. But, it's more about how your customer truly feel about you when all is said and done. The fact is, customers are more sceptical than ever. Hence, banks need to examine their brand's essence to gain customer loyalty and trust in the digital world. Banks that are trusted by its customers will win the survival race in this digital age. Hence you need to create long-term relationship with your customers. For which you should create connections where in you : Provide insight about what your prospects and customers are experiencing and what you are offering. Explain how your offering is in their best interest rather than your own. Position your offer as the ultimate solution to your prospects and customers. Appeal to your prospect’s emotions by explaining how your product or service will improve their life. Present your offer as a complete package — think of it in a way that your prospective customers/clients will expect more from your brand. 2 Gen Z and its chronicles Gen Z is becoming the new consumer group; unlike previous generations, they are poles apart in several ways. Most importantly, Gen Z is tech-savvy and digitally sound. Gen Z grew up in a world that was moving into massive digitalization, immersed in technology and was taught to use computers early. They grew up understanding how technology can benefit their lives; they know how to use their phones and computers to stay in touch with their friends and use social media to stay updated with the latest information and breaking news. But, more than any other generation, Genz Z is concerned about their financial future. They have financially sound knowledge and are concious about their financial assets and investments from a very young age. It is this tech-savvy generation’s attention that banks need to address. Many Gen Z consumers are now coming of age in a time of uncertainty and instability. The financial crisis of 2008 and the ensuing Great Recession caused a financial panic, and many people lost their homes, their jobs, and even their savings. Gen Z, who grew up in this uncertain time, has come to view finance as an essential life skill. Therefore, they are more informed and educated about personal finance and far more willing to become financially savvy than previous generations. This is great news for banks and other financial institutions because it means that Gen Z consumers, more than any other generation, are willing to keep up with financial trends and seek out financial advice. As a result, banks can market their newest services like BNPL, to millennials. 3 Using CDP (Customer data platform) to target the perfect audience A customer data platform (CDP) is a platform that aggregates customer data produced by various channels and devices, and connects it with back-end systems. Companies may use CDPs to centrally store, manage, activate, and analyze consumer data. These platforms also provide connectivity with third-party solutions like Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), or Marketing Automation (MA), allowing marketers to use the CDP's capabilities while creating customer-centric strategies. Modern day marketers need to understand consumer behavior better, detect patterns, and customize customer experiences to connect with customers. A deep understanding on these metrics will help marketers reach their customers diligently, for which marketers need CDP. A CDP may also let marketers gather data from third-party websites or mobile apps. Across financial services and fintech, CDPs have the potential to revolutionize the way that consumers view and interact with their companies. A CDP, or customer data platform, gives marketers access to a centralized platform, or hub, containing a person’s data (including its identity), interests and preferences, and information about their interactions across the company’s many channels. Marketers can gather, analyze and act on this data, using many tools; to drive personalized experiences and communications. We're seeing technology advance and be commoditized in a way that we've never really seen before with the advancements around artificial intelligence and cloud capability, or even the revolution that we're seeing within the core banking sphere is really changing what financial services actually means.” -David Brear, CEO and Co-founder at 11:FS 4 Self Service We are living in the age of customers. Today, consumers expect greater accessibility and personalization. They want banking tailored to their needs. They want the ability to talk to humans when they need – but also, they want banking and payment tools that are intuitive and simple to use. Consumers also want to speed things up as much as possible. They want everything to be quick and convenient. They want banking to be personalized and contextual. As we look at the future of digital banking, we see consumers interacting in a much different way than they did just a few years ago. It’s no longer about meeting every expectation but about anticipating them. For exampleif you know that your customers usually travel on Tuesdays, you can suggest that them to pay their bills on their return. This way, you won’t miss a payment, and your customers won’t incur late fees. With more and more banks moving their infrastructure to the cloud, self-service banking is getting increasingly sophisticated. When we talk about self service we are not talking about ATMs or Digital wallets. We’re talking machine learning and artificial intelligence, cognitive technologies, and conversational interfaces. We’re talking about banking in 2022 and beyond. Its high time you analyse how your bank is performing in terms of the above-mentioned trends and start investing in them accordingly. 5 FAQs Q. What are the new challenges faced by modern banks? A. Today, customers demand "quick" access to their money, and regulators are concerned that banks aren't providing enough services. So, banks have responded by making greater use of credit cards, debit cards and other financial products. As banks have moved into other areas, they are faced with new challenges like: Increased competition. A cultural shift. Regulatory compliance. Changing business models. Rising expectations. Q. What are some trends in modern banking? A. The goal is to give customers a seamless and convenient digital banking experience. To stay competitive, banks are looking for ways to innovate. Some are turning to technology, including AI, machine learning, and automation. Others are adopting new strategies, like opening innovation labs and inviting outside entrepreneurs to test new products. Q. Can banks use the public cloud? A. Yes, banks can use the public cloud. , banks are taking note of the benefits of cloud computing. The cloud-based software-as-a-service model, for example, allows banks to focus on their core banking operations and outsource the management, maintenance and support of their IT infrastructure. Q. What is the future of banking? A. The Digital Revolution is changing the way people do their banking. Given the right access to the right information at the right time through digital means, customers are increasingly shifting transactions online. This has empowered them to make better financial decisions. { "@context": "", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What are the new challenges faced by modern banks?", "acceptedAnswer": { "@type": "Answer", "text": "A. Today, customers demand \"quick\" access to their money, and regulators are concerned that banks aren't providing enough services. So, banks have responded by making greater use of credit cards, debit cards and other financial products. As banks have moved into other areas, they are faced with new challenges like: Increased competition. A cultural shift. Regulatory compliance. Changing business models. Rising expectations." } },{ "@type": "Question", "name": "What are some trends in modern banking?", "acceptedAnswer": { "@type": "Answer", "text": "The goal is to give customers a seamless and convenient digital banking experience. To stay competitive, banks are looking for ways to innovate. 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How Banks Should Use Buy Now Pay Later To Increase Their Customer Base

Article | October 7, 2021

The Buy Now Pay Later (BNPL) phenomenon is gaining momentum around the world. BNPL services give consumers who do not have access to credit the ability to purchase goods and services with no deposit and to pay for goods and services over time. However, while banks, consumer credit providers and alternative credit providers will benefit from BNPL services, they also introduce challenges for financial regulators, existing providers in related markets and banks themselves. Banks need to make sure that they are ready for a new type of competition. Larger retail banks seem to have added BNPL business to their portfolio already. Smaller banks will be forced to enter the market, either by acquiring a BNPL provider or rolling up the sleeves internally. In any case, it’s time that banks get rid of their prejudices and get on board with this consumer-friendly innovation that will ultimately benefit them by providing an influx of new customers, at least in the long run. For a complete understanding of buy now pay later, we should first look at the traditional financing models that banks and fintechs use to lend credits. These financing technique is known as point-of-sale or POS. Let’s take a look on POS below. How POS (Point-of-sale) Financing services work: Traditional POS financing is a model that has been around for decades. Most consumers are familiar with its most basic form: pay now, pay later. With POS financing, a customer signs up for credit to buy a product, typically for a portion of its full price. Some POS financing programs require no down payment. Once the customer has made all payments, they become the owner of the goods. POS financing works by financing the full price of the product, not a portion of the price. This means the customer pays the full purchase price of the item, plus interest. While POS financing has been popular for decades, it has faced some challenges. The payment model doesn't cater to customers who can't afford to pay the full purchase price upfront — these shoppers are often low-income or first-time-buyer customers. POS financing also requires shoppers to make large payments right away, which can be difficult for them. “The banking “industry” is changing rapidly – almost on a daily basis. However, those changes are not affecting people as much as we may think, particularly the underserved and unbanked.” -Steven Rosamilia, CEO at IMEX USA How BNPL helps customers: BNPL, or "buy now, pay later," payments enables customers to pay for their purchases over time, interest-free. BNPL payments don't appear on a customer's credit profile, so it doesn't affect their credit score. Here are some of the major points where BNPL helps customers: BNPL payments give customers the ability to buy now and pay later without accruing interest. BNPL payments are typically not fixed and fluctuate based on a customer's ability to pay over time. BNPL payments often appear in the form of layaway, credit extensions or installment loans. BNPL payments may attract customers who want to own products but don't have the money upfront. BNPL payments also work well for customers who want to spread out payments over time. How BNPL is different than other POS lending services: BNPL is an alternative payment technique offered by the payment service provider to businesses. Payment service providers use credit lines provided by banks and credit card companies to offer installment loans to customers. Unlike conventional POS financing, BNPL focuses on consumers' ability to purchase a product rather than their ability to repay their loan. This is achieved by classifying consumers into different groups based on their creditworthiness and offering consumers an installment loan with payment periods that vary based on their creditworthiness. As a result, payment service providers use BNPL as a risk-based financing technique. The payment service provider considers consumers' creditworthiness by classifying them into different consumer groups, such as "prime" consumers, "sub-prime" consumers, and "near-prime" consumers. These consumer groups are similar to credit profiles used by conventional credit card companies. With BNPL, businesses can request a payment profile classification from their business service provider. The payment profile classification determines the installment loan payment schedule that the consumer receives. Businesses can request a payment profile classification from their business service provider. The payment profile classification determines the installment loan payment schedule that the consumer receives. For checking your credit-worthiness before lending you BNPL, service providers may check consumer’s payment history, income, job stability, and other major factors. The financial service provider then use these factors to determine the installment loan payment schedule that the consumer receives. What features BNPL brings to the table for Merchants: Buy Now Pay Later is a new way to process payments. It's for young adults with shaky credit. The option lets merchants accept credit or debit cards but defer the payments. It lets merchants offer customers a low payment schedule, typically 6 to 24 months. But it's different than payment plans. With BNPL, there's no interest, no hidden fees, and no penalties for not paying all at once. BNPL works with all credit cards, not just Visa or MasterCard, and payments are processed securely through Authorized pages. BNPL increases conversion and sales by 20% for merchants while boosting average order value by 60%. For customers, BNPL gives them access to the credit they otherwise wouldn't have. And for merchants, BNPL means more conversions, more sales and more repeat customers. BNPL is offered by a handful of digital storefronts, including Best Buy, Kohl's, and Walmart. But it's a new way of doing business that allows both parties to benefit from the deal (compared to 2.5 percent for a credit card transaction). Why should Financial Institutions accept BNPL: Amazon's Buy Now Pay Later (BNPL) program is both a blessing and a curse for retailers — a blessing as it offers them a way to boost sales by attracting shoppers who are price sensitive, and a curse because it threatens to erode bank's main business. Amazon's BNPL program has only been around for two years, but it has already become a crucial part of the site's business model. The program gives people the option to buy products on Amazon with deferred payment terms. Customers purchase the product, but they aren't charged to agree to a 90-day payment plan until later. While that's far less than the average credit card payment period — 25% of Americans carry credit card debt — BNPL has become popular enough with Amazon shoppers that it has shrunk Amazon's average purchase amount by $7.77, according to one report. That's a significant hit. Amazon's BNPL program may be taking Amazon's main business, online sales, down a notch, but the banks that have issued BNPL cards aren't worried. That's because BNPL cards, like credit cards, are financing. And financing today looks different than financing did even five years ago. Many consumers, especially Gen Z, prefer to buy with credit and postpone payments. This shift in consumer preferences has major implications for banks. Banks issued financing to safe, creditworthy customers who wanted to buy now and pay later when credit cards were first introduced. But bank lending practices have changed over the years, and today many consumers use credit cards to finance products they might otherwise buy with cash. How can Banks integrate BNPL in their lending services BNPL is a fast-growing segment of the lending market. In 2015, BNPL made up 15.2% of all consumer credit originations and grew to $12.1 billion, according to the Federal Reserve Bank of New York. BNPL's share grew from 8.4% in 2014 to 14.7% in 2015, according to Experian. A BNPL strategy allows banks to ride the wave of increased consumer debt by managing their balance sheet more aggressively. This helps stabilize revenues and boosts the profitability of loans as banks can charge higher interest rates. While BNPL loans often come with hefty price tags, lenders can minimize their losses by structuring BNPL loans as an asset purchase rather than a loan sale. First, banks have to make sure they can fund the loans, either with their balance sheet or with funding from a non-bank lender. Second, banks have to decide whether the loan will be purchased directly or indirectly. Cross River Bank is currently riding the BNPL trend with this model by providing Affirm with funding capacity. The model is safe as BNPL firms often purchase those loans after origination, but it also caps the potential gains banks can earn as the fee is often a small percentage of the total origination. How can banks initiate marketing their buy now pay later services? First, banks need to be agile and go after merchants that already have relationships with customers. Fintechs, on the other hand, must convince merchants that their service, regardless of its costs, is worth paying. There are obviously some similarities. Both must win over merchants. But they also have different advantages. Fintechs don't have existing relationships or established customer bases, so they must build both from scratch. Fintechs, however, have an advantage over banks in that they have the technology. In addition, fintechs can integrate their solutions into existing e-commerce systems, giving merchants an out-of-the-box, easy-to-deploy solution. This, in turn, makes fintech more attractive to merchants. Fintechs can also target specific markets. For example, some banks sell online merchant accounts, but their service is often limited to larger merchants with more established distribution networks. Fintechs, on the other hand, can target smaller merchants, giving them an approach that's better suited to the needs of smaller businesses. Fintechs can also target specific niches. A fintech that targets small businesses, for example, could focus on those that sell high-priced goods online. Fintechs don't have to build their distribution networks, either. Instead, they can use existing online channels like Amazon, eBay and Alibaba. Of course, fintechs can also sell directly to merchants, but this approach requires additional sales and marketing efforts. Fintechs can also build their distribution networks. They can use a direct-to-consumer model, selling directly to their customers. This approach is best suited for fintech that is sells online merchant accounts and works for fintech that targets specific markets. The Takeaway BNPL programs have a critical role in financing trade and industry and financing small and medium-sized enterprises (SMEs). For this reason, BNPL programs should be an integral part of banks’ lending portfolios. Banks should optimize the utilization of BNPL programs. At the same time, the regulatory framework for BNPL programs needs to be revised. The business models of BNPL programs should be standardized and standardized products should be available. At the same time, the regulatory framework for BNPL programs needs to be revised. FAQs What is buy now pay later? Buy now pay later, as the name suggest, is an option Fintechs give you to purchase a product and pay for it after a certain amount of time. It works like a credit card payment, but it doesn’t charge you interest. Does buy now pay later affect credit score? No. Buy now pay later does not affect your credit score as long as you pay your dues timely. It is constructed in a way that you won’t have to worry about your credit score. However, banks may see your credit score before giving you BNPL service. Why was I not eligible for buy now pay later? Financial services or banks check your credit-worthiness before lending you the services of buy now pay later. They may check your payment history, income, job stability, etc. So before applying for BNPL, make sure you have a strong credit-worthiness. What are the alternatives to buy now pay later? You can use your credit card the same way as buy now pay later, but your interest-free days would only last till they bill you. You can also opt for interest free deals on purchases from e-commerce store.

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Westlake Financial Services

People. Purpose. Passion. These are the hallmarks of Westlake Financial Services, and have been since our inception in 1978. Westlake has over twenty five years experience in sub-prime automotive financing, helping car dealerships do what they do best: sell cars. Our unique credit approval software (known as "The Buy Program ©"​) allows dealers to get an on-the-spot loan approval with out waiting for callbacks, fax-backs, or rehashes.