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IMPLEMENTING MACHINE LEARNING: FIVE LESSONS FROM FINANCIAL SERVICES ORGANISATIONS

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IMPLEMENTING MACHINE LEARNING: FIVE LESSONS FROM FINANCIAL SERVICES ORGANISATIONS

By Steve Wilcockson, Industry Manager – Financial Services, Mathworks.

Steve Wilcockson

Steve Wilcockson

The financial services industry has long been a hub for technological innovation. So it’s not surprising that banks, asset managers, insurers and even supervisors are exploring how they can apply machine learning.

Interest in the technology has grown rapidly over the last few years.  For example, back in 2014, we surveyed financial professionals and found only 12% were using machine learning in their workflows. When we repeated the study in 2016 with risk and quant analysts attending our London computational finance event, the number using machine learning had almost quadrupled to over 40%.  What’s more, half of those who said they weren’t doing anything today said they planned to start projects within 12 months.

This appetite to embrace machine learning is driven by a strong belief that the future of the industry will be decided by financial computing engineers, and their algorithms. But, stripping away the headlines and hyperbole, what’s the current state of play?

Today, the application of machine learning is progressing at different rates across the industry segments. Buy-side players, competing to increase returns and take (sensible) risks,are increasingly applying machine learning techniques as an extension of their factor analysis suites to differentiate from peers and rivals. One asset manager we know well uses machine learning to determine correlation and predictive trends across macroeconomic, credit, liquidity, risk and money flow factors. This allows them to better understand asset class performance trends, with some of their portfolios outperforming benchmarks by 100 basis points.

The sell-side has a more nuanced “risk management-first” view, oriented around task, skills and, importantly, regulation. Data science and big data teams, sometimes born from Quant departments and other times IT, often focus on “test” problems, such as fraud detection, money laundering investigation, or modelling customer activity. However, methodologies in some cases are impacting mainstream functions, for example as adaptable means of modelling credit risk dynamics. Regulators can be sensitive to the use of such approaches because in some instances machine learning approaches are considered “black box”, but where they are well-documented and discussed, they are increasingly accepted.

The challenges of bringing machine learning into a complex financial business remain daunting. This can be due to the quantity, type and variety of data being analysed. Other challenges might include determining the best model approach for the problem at hand, and ensuring participants are comfortable with its role in the process.

So, as the industry evolves its use of machine learning, what lessons can we learn to generate success?

  1. Understand your mandate

Like any complex project, set clear goals about what you want to achieve, and the role of machine learning in achieving them. If considering developing “intelligent” robo-advisers, for example, how intelligent do they need to be, when and how will difficult questions be passed to a human, what human roles will be replaced or augmented, and how will your customers respond to robo-advice? Most importantly, what will the human/automation interface look like? The head of one Wall Street hedge fund pushing ahead with greater use of machine learning summed this up well as “No man is better than a machine. And no machine is better than a man with a machine.”

  1. Pick low hanging fruit but be strategic

Running machine learning in a standalone greenfield project can be a sensible first step, but it may be hard to demonstrate sufficient gains and pitfalls. Incorporating machine learning methods into existing processes can be more demonstrable. Consider running shadow projects to compare and contrast performance with existing ones, for example to assess whether retail credit requests such as mortgage extension requests may reflect default conditions or are reasonable requests for (reasonable) asset financing. This allows relative quantifiable benchmarking. Consider multiple measures too. Accuracy is one thing, but model lifecycle might be another. One advantage to both bank and regulator for example in the adoption of so-called bagged decision trees as a means of modelling credit ratings adjustment might result in a 4x to 10x model lifetime compared with a less adaptable traditional approach. This can save development and maintenance time and effort. As one machine learning expert at a major international bank recommends, tackle low hanging fruit to achieve 80 percent of the impact in five percent of the time.

  1. Together, Stronger

Machine learning might appear to be a subject for a small band of technologists and thus the preserve of the mathematician and computational elite, but recognize that others bring relevant skills.

Cross functional teams matter hugely. Software engineers bring the ability to speed up model creation and implementation gains; project managers bring project lifecycle management; model users and domain experts bring the ability to state design requirements and suggestions that lead to differentiating and useful insights. To ensure all this activity doesn’t fly apart, communication, coordination and support across the functions matter,keeping the team tight and successful.

Yes, your machine learning expert may be your team’s star player but it takes a strong performance from the whole team to ensure successful execution.  You need to prepare to manage, and overcome failure.  Similarly, it’s important to continue to learn, adapt and improve, both as individuals and as a team. Your team needs to consolidate around its own slick cross functional working environment, developing models and implementing them rapidly.

Your team also must acquire new blood, to develop, apply, implement and interpret new methodologies better, to fill gaps, acquire code and expertise from beyond your organizational boundaries. Kaggle, who run hundreds of data science competitions, can provide competitive leader boards for driving energetic internal problem solving and tapping hidden talents for a machine learning project. It’s quite possible to incorporate these contributions into your own projects.

  1. Make it Human

Ultimately user engagement is key. Machine learning is useless without meaningful output. Machines may know best in many cases, but value comes from human interpretation and use. Humans are visual creatures, so consider “simple” visualization as much as you do the most complex model. Avoid misleading graphical representation and do consider subtle nuances that can communicate insights most forcefully. As one big data lead in a global bank stated,“a good font in a visualization is like being more mentally engaged when hearing a nice accent”. Such good aesthetics means key stakeholders can make a faster mental connection with what they’re being shown when sometimes fully and quickly understanding the most complex model output can be challenging.

  1. No one size fits all

Every task is different, so use platforms and tools that you can immerse yourself in to understand those differences, to import whatever data-set you need, to try out multiple algorithms quickly and easily perhaps as part of near-at-hand applications. Cross-validate and test, parallelize to speed-up model execution in clouds and on GPUs, and get trustworthy solutions out to the people who need them, as web applications, in spreadsheets and as database components.

But know your risk. Bad tools add risk as much as good tools can facilitate faster time-to-market and enhance productivity. Know which tools you are using, how trustworthy the routines are, and how comprehensive the supporting help and documentation is.  Consider how efficient their algorithms are, and if faced with a difficult time-consuming task, how easily scaled is it in a distributed set-up. Software for developing and implementing machine learning and data science can be like the Texas rangers in the days of the Wild West, at worst toxic, dangerous and unpredictable and at best bring order to the frontier. As far as possible, seek your own order amidst chaos, yet also take care not to lose sight of the opportunity and the excitement of the unknown.

The financial services industry’s use of technology can be contradictory; fast-paced yet also conservative. Nonetheless, the sector is increasingly leveraging machine learning as part of a broad tool-set to address challenging problems, and build exciting new use cases.

Finance

Top 8 Tax Scams to Watch Out For

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Top 8 Tax Scams to Watch Out For 1

It is tax time and that means finding the best way to file your taxes and to get a refund of any amount you’ve overpaid. Unfortunately, tax time also means plenty of scammers are thinking of new and clever ways to try and get their hands on your money or on your personal information (which they can use to get money).

Those who specialize in IRS tax scams are clever and can be very convincing. Your first line of defense is to always know what to be looking for in terms of common tax fraud in order to avoid being another victim.

8 Most Common Tax Scams

Protecting yourself from IRS tax scams can be tricky if you’re not aware of what the threats are. A good tax scam seems legitimate, and that is what makes them dangerous. Always be on the lookout for the eight most common tax scams, including:

  • IRS Phone Scams
  • Fake IRS Emails
  • Fraudulent Tax Preparers
  • Fraudulent Tax Refunds
  • Fake Charities
  • Set Up Offshore Accounts
  • Empty Promises
  • Frivolous Returns

To know what exactly you need to watch out for, let’s look at them in more detail.

1. IRS Phone Scams

If someone calls you claiming to be from the IRS, it is almost certainly one of many IRS phone scams. The IRS will never call you to demand money for back taxes or to confirm your personal information, so be immediately alert. Never give personal information over the phone, and don’t head to the bank to follow the demands for money.

If you do wind up on the end of an IRS phone scam, don’t become flustered by aggressive tactics by the fake “agent”. They are good at sounding threatening and demanding information or payments. Remain calm and ask for contact information. Tell the scammer you’ll call them back with the information. Either the scammer will give you fake information or he will work to avoid leaving any information at all. Regardless, don’t call him back. Simply report the call to the local police or the IRS.

2. Fake IRS Emails

Another very common fake IRS scam is phishing, or sending fake IRS emails, in a ploy to gather personal information. Fake emails will look authentic and will ask you to click on a link or to log in to a fake IRS website. The purpose of these emails is to simply gather your personal information to be used for other fraudulent purposes.

Just like with IRS phone scams, you should be immediately wary if the IRS appears to send you an email. The IRS does not contact citizens through email. All official IRS communication will come through standard mail. If you do find a fake IRS email in your inbox, forward it to the IRS. The IRS investigates these scams and has a dedicated email address for this very purpose: [email protected].

3. Fraudulent Tax Preparers

Some scam artists show up in a suit, open a storefront and offer to prepare your tax return for you. These tax preparers appear by all accounts to be absolutely legitimate, and many go to great lengths to convince customers of their years of experience and authenticity.

As a fraudulent tax preparer, however, the person is not legitimate. The scam artist can use your tax return in many ways for his own benefit. He can inflate your refund and skim off the top. He can charge outrageous fees for filing on your behalf. He can file your return correctly this year and gather all of your information to make a fake return for his benefit next year.

If you are going to have someone else prepare your taxes, be sure to look carefully through tax service reviews. Tax service reviews are available on many different websites that offer feedback on companies and services. These reviews will give you a very good idea about the legitimacy of the business and the reliability of the preparer. If a company doesn’t have any tax service reviews on any website, like e.g PissedConsumer.com, or BBB, that may be a sign that it’s a pop-up company that will disappear as soon as the scammer has what he wants.

4. Fraudulent Tax Refunds

Another very popular tax scam starts well before the tax season. To file a fraudulent tax return, the scammer must gather all pertinent personal information including a social security number. He then uses the information he gathered to file a fake tax return on your behalf. Naturally, he’s not going to send you the refund he’s claiming – that goes into the scammer’s pocket.

The best way to prevent a fake tax return is to guard your personal information close at all times. If nobody is able to steal your identity, they can’t file a tax return. Another good step is to file your own tax return as early as possible. That way, even if your information was stolen somehow, you will get your refund correctly and the IRS will be alerted when someone files a second return using your information.

5. Fake Charities

Charitable donations are tax-deductible if you’re itemizing your deductions. This creates possibilities for scammers to take advantage of others who are looking to reduce their tax burden and increase their refund by making donations. Fake charities can take on many shapes and forms.

Some may appear conveniently around tax time or be affiliated with fraudulent tax preparers. The claim is that by donating to a fake charity you will help others and reduce your own tax liability. Instead, you’re giving someone free money and you won’t be able to deduct the donation as it’s not a real charitable organization. Other fake charities involve you in a scam by promising to give you back your donation as soon as the tax return is filed, for example. It goes without saying that claiming a donation you didn’t actually make is tax fraud and highly illegal.

At the advice of his tax preparers, a famous country singer Willie Nelson moved some of his money into tax shelters and charities to help reduce his tax bill. The IRS grew suspicious of the moves and investigated. In one of the most famous IRS cases in the United States, Willie Nelson was hit with a tax bill in the millions when his charities and shelters were found to be invalid.

Willie didn’t have the funds to make the payments, so the bill continued to grow until the IRS finally grew so frustrated they raided and seized all of Willie Nelson’s properties including a recording studio, a ranch, and his home. Even that wasn’t enough to pay the bill, so eventually, Willie made a deal with the IRS. He recorded an album and all proceeds from that album went directly to the IRS to whittle away his debt. Willie did file suit against the accounting firm that advised the tax shelters in the first place, but the two parties settled out of court.

6. Set Up Offshore Accounts

Some tax scams sound good but require your participation in illegal activities. For example, you may meet an unscrupulous tax “professional” who offers to help you move some of your money into an offshore account.

This sounds legitimate as many people use offshore accounts for valid reasons, but by moving your funds into an offshore account with the intent of hiding that income from the IRS, you’re committing tax fraud. Additionally, if you’re working with a shady professional, it’s highly likely that neither you nor the IRS will see your extra income ever again. And you can still wind up with a legal case with your money stolen and gone.

7. Empty Promises

The tax preparer who encourages you to sign a blank tax form is nobody you want to work with. These preparers encourage you to simply sign the form because he or she is going to work out the numbers for you so that you can get the highest possible refund. If you do this, you are almost certainly subjecting yourself to tax filing scams.

Signing a blank tax form is potentially worse than simply signing a blank check for a stranger. Not only are you at risk of losing your personal information and any refund you might be owed, but you are also at risk of legal action by the IRS for signing your name on a refund that is almost certainly going to contain false and fraudulent information.

8. Frivolous Returns

The IRS sees a ridiculous number of what they call “frivolous returns” every year. A frivolous return is a tax return that is filed with the intent of simply wasting time. These frivolous claims have already been thrown out in court, so filing a tax refund making a frivolous claim is simply opening yourself up to additional action by the IRS including fines of at least $5,000. The top “frivolous claims” include:

  • Refusing to pay taxes on moral or religious grounds
  • “Opting out” of paying taxes
  • Invoking the First Amendment to “protect” you from taxes
  • Claiming only Federal Employees pay federal taxes
  • Claiming you have no income and therefore no tax liability (when you clearly do)

Top 3 Tips on How to Protect Yourself from IRS Tax Scams

Protecting yourself from tax fraud is a matter of being vigilant and mindful that there is always a possibility of something going wrong. Work with a trusted advisor or study up and file taxes yourself to avoid the uncertainty of allowing others to handle your financial matters. Often a bit of knowledge goes a very long way.

1. Know How the Tax System Works

One of the most common negative IRS reviews is that the tax refunds aren’t released immediately. In many IRS complaints, customers complain that they don’t get their refunds immediately.

While frustrating to wait, the IRS is usually very clear about processing times and has never sent refunds immediately after the filing window opens. The government doesn’t move quickly and reviews of documents and financial information submitted in your returns are necessary.

Additionally, relying on others to help you file your taxes every year can open you up to the possibility of fraudulent activities. Reviewing the tax codes and reading through the laws and requirements may not be exciting, but it will give you at least a basic understanding of how the process works so that you can look out for problems if you are trusting someone else with your information and money.

2. Always Read Carefully

The safest way to file your taxes is to do them by hand on the original IRS paper forms and to mail them using certified mail. Many people don’t choose to do this, however, as it can be very tedious and confusing if you do not know the tax system backward and forwards.

Instead, many filers rely on tax software and paid tax preparers. When using software or allowing someone to use the software on your behalf, it never gets too comfortable. There might be hidden fees in the software or glitches to overcome.

Reviewing choices carefully as the software takes you from screen to screen is a good way to avoid accidentally accepting hidden fees. Another option to avoid paying for fees you aren’t comfortable with is to simply abandon the return on one piece of online software and to try again with another – there are multiple tax return software options available.

3. Always Look for Tax Filing Scams

If you always expect to find a scam, you’ll never be surprised when one appears. Even tax preparers who have been in business for years can have some deceptive business practices that others assume are necessary or haven’t noticed them at all.

Tax time can be exciting if you’re entitled to a large refund, but it can be stressful if you don’t feel in control of the tax filing process. Educate yourself on the risks and tax scams that exist, and always exercise caution when choosing a method to file your taxes. Your personal information is closely tied to your money, so protecting both of them is often simply a measure of keeping your eyes wide open and using your knowledge to avoid traps and scams.

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These 5 Payments Trends Once Seemed Revolutionary. In 2021, They’ll Continue to Become the Norm

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Real-time payments – mitigating the security risks to capitalise on the opportunities

By Warren Hayashi, President, Asia-Pacific, Adyen.

The pandemic forced brands to transform their businesses in ways that are here to stay

After a year of such great uncertainty, attempting to predict the future may seem risky. But even as brands and retailers faced unprecedented upheaval in 2020, one constant has held true. The pandemic has accelerated trends toward digitisation—and that’s as true in payments as in so many other areas of business and society. The stark reality of needing to avoid close contact with others has driven transformations for retailers and brands in a matter of months that in the past might have taken years. In the process, behaviours and expectations have changed for good.

As 2021 begins, much uncertainty remains but we feel confident that the digital transformation of payments will only get faster. Even after the pandemic has receded and consumers have the option to go back to their old behaviours, many won’t. The rapid increase in e-commerce seen under COVID-19 will persist, especially among previously digital-hesitant consumers. Merchants can no longer assume that their digital customers are limited to younger, tech-savvy shoppers. As brands have shown flexibility during the pandemic, consumers have also come to expect the flexible arrangements to continue. On that note, these are the key trends in payments that should be top-of-mind for brands and retailers in Singapore and Asia Pacific in 2021:

  1. Contactless will extend its reach into every corner of retail

From the start, the pandemic forced merchants to find ways to minimize the amount of physical contact necessary to complete a transaction. Customers and workers alike sought to avoid handing over credit and debit cards, touching keypads, and handling cash. According to our 2020 Agility Report 58% of APAC respondents preferred to use contactless payment methods because of hygiene concerns.

Our data also showed that the use of services such as Apple Pay and Google Pay has significantly increased over the last year too. Research from Kantar reiterates this, revealing that the frequency of e-Wallets transactions in Southeast Asia rose from an average of 18% pre-COVID-19 to 25% post-COVID-19[1], indicating a shift from one payment method to another.

In the post-pandemic world, the transition to contactless will only become more widespread now that the bar has been raised among consumers for what checking out can be, from one-click payments to same-day delivery options. Not to mention, the value of QR codes has also been made apparent in anchoring a seamless experience, not just at point-of-sale but at multiple points along the customer journey too, such as viewing menus and placing orders. The pandemic may have driven the change in behavior, but the superior user experience will cement contactless as the new normal.

  1. The distinction between offline and online will fade into irrelevance

As countries went into different forms of lockdown, many shoppers were unable to enter brick-and-mortar stores throughout 2020. Unifying offline and online became an issue of survival for retailers, who quickly pivoted to make app-powered deliveries and self-pick up options a reality.

Even while most physical stores in Singapore have opened their doors to consumers again, the digital infrastructure will remain in place. Many shoppers continue to prefer the convenience of deliveries and expect the options to continue, and retailers will find they’re able to forge better customer relationships thanks to the rich data generated by digital transactions.

One of the biggest learnings for the industry is the need to rethink the traditional split between offline and online stores. With lines increasingly blurred, retailers will benefit from adopting a unified commerce approach where brand interactions on and across all channels are important.

  1. The membership model will reign in retail and also in food and beverage

The membership model is another emerging trend for 2021. Amazon Prime is a great example of this, where customers pay an annual fee that in effect encourages them to buy more from Amazon in an effort to ensure they’re getting their money’s worth from their Prime memberships. Quick-serve restaurants especially are seeking to seize some of that flywheel effect. In addition to improved incremental spend, membership programs enable QSRs to get to know their customers in ways that were never possible when they were just anonymous faces standing in line.

Meanwhile, subscription passes encourage loyalty and more frequent use. Our 2020 Agility Report found that 38% of Singapore respondents (compared to 27% in APAC and 22% in Europe) signaled their interest in using these for products, including food passes, to reduce the amount of times they need to shop. Expect to see more retailers offering memberships in 2021 as brands seek to own the customer relationship and the data that goes along with it.

  1. Installments will become an everyday way to pay

The twin forces of increased convenience and tightened household budgets have brought pay-by-installment options mainstream, a trend that will only grow in 2021. Machine learning algorithms have become more adept than ever at assessing risk instantaneously, making it easy to offer “buy now, pay later” options right at checkout. For small and mid-ticket items, shoppers know that, say, instead of paying $100 now, they’ll pay $25 per month for four months. That kind of transparency makes it easier for shoppers on the fence to commit, which appeals to merchants hoping to avoid the dreaded abandoned shopping cart.

In 2021, providers of “buy now, pay later” options themselves will start to diverge, as some focus on higher-end, multi-year agreements, while others seek to offer installment plans for shopping baskets as small as $50. For households increasingly accustomed to paying by the month for everything from streaming services to food delivery premium memberships, installment plans start to look like subscriptions that just happen to have a fixed end date.

  1. The checkout-less experience will draw shoppers back to brick-and-mortar

In 2020, the appeal of an in-store experience offering limited human contact took on a new dimension, accelerating interest in doing away with the checkout counter altogether. For instance, in Singapore, BHG is looking to expand its endless aisle offering. By using interactive screens in-store, customers are able to check on inventories across all of BHG’s stores and e-commerce platform and can opt to have items to be delivered directly to their homes.  Post-pandemic, shoppers will still find appeal in the human touch. The physical store continues to be relevant, especially in Asia Pacific and eliminating checkout counters frees staff to interact with shoppers in a more personal way, while also making lines a thing of the past.

In 2021, more stores will find various ways to make checkout a less prominent part of how people shop in-store. Multiple providers are creating their own versions of checkout-less experiences, where instead of going to the counter, customers will scan their items with their phones’ cameras, pay via app, and head out the door—a combination of increased trust and decreased friction that helps cultivate customer loyalty. In the case of Love, Bonito in Singapore, if customers are unable to find a particular item in store, they can go to an iPad within the premises, buy it online and have it shipped to their homes.

Across the five trends, this paradigm shift in the retail sector is underpinned by the under-tapped potential of technology to elevate the customer experience. Looking ahead in the new year, we expect retailers to increasingly harness digital solutions. Not only does this streamline operations, it also gives retailers the flexibility to pivot in line with changing preferences, and provide a seamless consumer journey across multiple channels.

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Bitcoin heads for worst weekly loss in months

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Bitcoin heads for worst weekly loss in months 2

By Tom Westbrook

SINGAPORE (Reuters) – Bitcoin wavered on Friday and was heading toward its sharpest weekly drop since September, as worries over regulation and its frothy rally drove a pullback from recent record highs.

The world’s most popular cryptocurrency fell more than 5% to an almost three-week low of $28,800 early in the Asia session, before steadying near $32,000. It has lost 11% so far this week, the biggest drop since a 12% fall in September.

Traders said a report posted to Twitter by BitMEX Research https://twitter.com/BitMEXResearch/status/1351855414103715842 suggesting that part of a bitcoin may have been spent twice was enough to trigger selling, even if concerns were later resolved.

“You wouldn’t want to rationalise too much into a market that’s as inefficient and immature as bitcoin, but certainly there’s a reversal in momentum,” said Kyle Rodda, an analyst at IG Markets in Melbourne, in the wake of the BitMEX report.

“The herd has probably looked at this and thought it sounded scary and shocking and it’s now the time to sell.”

Bitcoin was trading more than 20% below the record high of $42,000 hit two weeks ago, losing ground amid growing concerns that it is one of a number of price bubbles and as cryptocurrencies catch regulators’ attention.

During a U.S. Senate hearing on Tuesday, Janet Yellen, President Joe Biden’s pick to head the U.S. Treasury, expressed concerns that cryptocurrencies could be used to finance illegal activities.

That followed a call last week from European Central Bank President Christine Lagarde for global regulation of bitcoin.

Still, some said the pullback comes with the territory for an asset that is some 700% above the 2020 low of $3,850 hit in March.

“It’s a highly volatile piece,” said Michael McCarthy, strategist at brokerage CMC Markets in Sydney. “It made extraordinary gains and it’s doing what bitcoin does and swinging around.”

Second-biggest cryptocurrency ethereum intially slipped to a one-week low on Friday before rising 6% late in the Asia session to $1,177.

(Reporting by Tom Westbrook; editing by Leslie Adler & Simon Cameron-Moore)

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