Who Scams The Scammers? Meet the Scambaiters

Police struggle to catch online fraudsters, often operating from overseas, but now a new breed of amateurs are taking matters into their own hands.

Three to four days a week, for one or two hours at a time, Rosie Okumura, 35, telephones thieves and messes with their minds. For the past two years, the LA-based voice actor has run a sort of reverse call centre, deliberately ringing the people most of us hang up on – scammers who pose as tax agencies or tech-support companies or inform you that you’ve recently been in a car accident you somehow don’t recall. When Okumura gets a scammer on the line, she will pretend to be an old lady, or a six-year-old girl, or do an uncanny impression of Apple’s virtual assistant Siri.

Once, she successfully fooled a fake customer service representative into believing that she was Britney Spears. “I waste their time,” she explains, “and now they’re not stealing from someone’s grandma.” Okumura is a “scambaiter” – a type of vigilante who disrupts, exposes or even scams the world’s scammers. While scambaiting has a troubled 20-year online history, with early forum users employing extreme, often racist, humiliation tactics, a new breed of scambaiters are taking over TikTok and YouTube. Okumura has more than 1.5 million followers across both video platforms, where she likes to keep things “funny and light”.

In April, the then junior health minister Lord Bethell tweeted about a “massive sudden increase” in spam calls, while a month earlier the consumer group Which? found that phone and text fraud was up 83% during the pandemic. In May, Ofcom warned that scammers are increasingly able to “spoof” legitimate telephone numbers, meaning they can make it look as though they really are calling from your bank. In this environment, scambaiters seem like superheroes – but is the story that simple? What motivates people like Okumura? How helpful is their vigilantism? And has a scambaiter ever made a scammer have a change of heart?

Batman became Batman to avenge the death of his parents; Okumura became a scambaiter after her mum was scammed out of $500. In her 60s and living alone, her mother saw a strange pop-up on her computer one day in 2019. It was emblazoned with the Windows logo and said she had a virus; there was also a number to call to get the virus removed. “And so she called and they told her, ‘You’ve got this virus, why don’t we connect to your computer and have a look.” Okumura’s mother granted the scammer remote access to her computer, meaning they could see all of her files. She paid them $500 to “remove the virus” and they also stole personal details, including her social security number.

Thankfully, the bank was able to stop the money leaving her mother’s account, but Okumura wanted more than just a refund. She asked her mum to give her the number she’d called and called it herself, spending an hour and 45 minutes wasting the scammer’s time. “My computer’s giving me the worst vibes,” she began in Kim Kardashian’s voice. “Are you in front of your computer right now?” asked the scammer. “Yeah, well it’s in front of me, is that… that’s like the same thing?” Okumura put the video on YouTube and since then has made over 200 more videos, through which she earns regular advertising revenue (she also takes sponsorships directly from companies).

“A lot of it is entertainment – it’s funny, it’s fun to do, it makes people happy,” she says when asked why she scambaits. “But I also get a few emails a day saying, ‘Oh, thank you so much, if it weren’t for that video, I would’ve lost $1,500.’” Okumura isn’t naive – she knows she can’t stop people scamming, but she hopes to stop people falling for scams. “I think just educating people and preventing it from happening in the first place is easier than trying to get all the scammers put in jail.”

She has a point – in October 2020, the UK’s national fraud hotline, run by City of London Police-affiliated Action Fraud, was labelled “not fit for purpose” after a report by Birmingham City University. An earlier undercover investigation by the Times found that as few as one in 50 fraud reports leads to a suspect being caught, with Action Fraud frequently abandoning cases. Throughout the pandemic, there has been a proliferation of text-based scams asking people to pay delivery fees for nonexistent parcels – one victim lost £80,000 after filling in their details to pay for the “delivery”. (To report a spam text, forward it to 7726.)

Asked whether vigilante scambaiters help or hinder the fight against fraud, an Action Fraud spokesperson skirted the issue. “It is important people who are approached by fraudsters use the correct reporting channels to assist police and other law enforcement agencies with gathering vital intelligence,” they said via email. “Word of mouth can be very helpful in terms of protecting people from fraud, so we would always encourage you to tell your friends and family about any scams you know to be circulating.”

Indeed, some scambaiters do report scammers to the police as part of their operation. Jim Browning is the alias of a Northern Irish YouTuber with nearly 3.5 million subscribers who has been posting scambaiting videos for the past seven years. Browning regularly gets access to scammers’ computers and has even managed to hack into the CCTV footage of call centres in order to identify individuals. He then passes this information to the “relevant authorities” including the police, money-processing firms and internet service providers.

“I wouldn’t call myself a vigilante, but I do enough to say, ‘This is who is running the scam,’ and I pass it on to the right authorities.” He adds that there have only been two instances where he’s seen a scammer get arrested. Earlier this year, he worked with BBC’s Panorama to investigate an Indian call centre – as a result, the centre was raided by local police and the owner was taken into custody.

Browning says becoming a YouTuber was “accidental”. He originally started uploading his footage so he could send links to the authorities as evidence, but then viewers came flooding in. “Unfortunately, YouTube tends to attract a younger audience and the people I’d really love to see looking at videos would be older folks,” he says. As only 10% of Browning’s audience are over 60, he collaborates with the American Association of Retired People to raise awareness of scams in its official magazine. “I deliberately work with them so I can get the message a little bit further afield.”

Still, that doesn’t mean Browning isn’t an entertainer. In his most popular upload, with 40m views, he calmly calls scammers by their real names. “You’ve gone very quiet for some strange reason,” Browning says in the middle of a call, “Are you going to report this to Archit?” The spooked scammer hangs up. One comment on the video – with more than 1,800 likes – describes getting “literal chills”.

But while YouTube’s biggest and most boisterous stars earn millions, Browning regularly finds his videos demonetised by the platform – YouTube’s guidelines are broad, with one clause reading “content that may upset, disgust or shock viewers may not be suitable for advertising”. As such, Browning still also has a full-time job.

YouTube isn’t alone in expressing reservations about scambaiting. Jack Whittaker is a PhD candidate in criminology at the University of Surrey who recently wrote a paper on scambaiting. He explains that many scambaiters are looking for community, others are disgruntled at police inaction, while some are simply bored. He is troubled by the “humiliation tactics” employed by some scambaiters, as well as the underlying “eye for an eye” mentality.

“I’m someone who quite firmly believes that we should live in a system where there’s a rule of law,” Whittaker says. For scambaiting to have credibility, he believes baiters must move past unethical and illegal actions, such as hacking into a scammer’s computer and deleting all their files (one YouTube video entitled “Scammer Rages When I Delete His Files!” has more than 14m views). Whittaker is also troubled by racism in the community, as an overcrowded job market has led to a rise in scam call centres in India. Browning says he has to remove racist comments under his videos.

“I think scambaiters have all the right skills to do some real good in the world. However, they’re directionless,” Whittaker says. “I think there has to be some soul- searching in terms of how we can better utilise volunteers within the policing system as a whole.”

At least one former scambaiter agrees with Whittaker. Edward is an American software engineer who engaged in an infamous bait on the world’s largest scambaiting forum in the early 2000s. Together with some online friends, Edward managed to convince a scammer named Omar that he had been offered a lucrative job. Omar paid for a 600-mile flight to Lagos only to end up stranded.

“He was calling us because he had no money. He had no idea how to get back home. He was crying,” Edward explains. “And I mean, I don’t know if I believe him or not, but that was the one where I was like, ‘Ah, maybe I’m taking things a little too far.’” Edward stopped scambaiting after that – he’d taken it up when stationed in a remote location while in the military. He describes spending four or five hours a day scambaiting: it was a “part-time job” that gave him “a sense of community and friendship”.

“I mean, there’s a reason I asked to remain anonymous, right?” Edward says when asked about his actions now. “I’m kind of embarrassed for myself. There’s a moment where it’s like, ‘Oh, was I being the bad guy?’” Now, Edward doesn’t approve of vigilantism and says the onus is on tech platforms to root out scams.

Yet while the public continue to feel powerless in the face of increasingly sophisticated scams (this summer, Browning himself fell for an email scam which resulted in his YouTube channel being temporarily deleted), But scambaiting likely isn’t going anywhere. Cassandra Raposo, 23, from Ontario began scambaiting during the first lockdown in 2020. Since then, one of her TikTok videos has been viewed 1.5m times. She has told scammers her name is Nancy Drew, given them the address of a police station when asked for her personal details, and repeatedly played dumb to frustrate them.

“I believe the police and tech companies need to do more to prevent and stop these scams, but I understand it’s difficult,” says Raposo, who argues that the authorities and scambaiters should work together. She hopes her videos will encourage young people to talk to their grandparents about the tactics scammers employ and, like Browning, has received grateful emails from potential victims who’ve avoided scams thanks to her content. “My videos are making a small but important difference out there,” she says. “As long as they call me, I’ll keep answering.”

For Okumura, education and prevention remain key, but she’s also had a hand in helping a scammer change heart. “I’ve become friends with a student in school. He stopped scamming and explained why he got into it. The country he lives in doesn’t have a lot of jobs, that’s the norm out there.” The scammer told Okumura he was under the impression that, “Americans are all rich and stupid and selfish,” and that stealing from them ultimately didn’t impact their lives. (Browning is more sceptical – while remotely accessing scammers’ computers, he’s seen many of them browsing for the latest iPhone online.)

“At the end of the day, some people are just desperate,” Okumura says. “Some of them really are jerks and don’t care… and that’s why I keep things funny and light. The worst thing I’ve done is waste their time.”

By:

Source: Who scams the scammers? Meet the scambaiters | Cybercrime | The Guardian

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Cyberthreats: The Emerging Fault Lines of the Nation State. Oxford University Press.

ISBN9780190452568. Fisher, Bonnie S.; Lab, Steven (2010). Encyclopedia of Victimology and Crime Prevention. Thousand Oaks, CA: SAGE Publications. p. 493.

ISBN9781412960472. “FBI 2017 Internet Crime Report” (PDF). FBI.gov. Federal Bureau of Investigation. May 7, 2018. Retrieved 28 August 2018.

“The Economic Impact of Cybercrime— No Slowing Down” (PDF). McAfee. 2018. Retrieved October 24, 2018. Goel, Rajeev K. (2020).

“Uncharitable Acts in Charity: Socioeconomic Drivers of Charity-Related Fraud”. Social Science Quarterly. 101 (4): 1397–1412. doi:10.1111/ssqu.12794. ISSN1540-6237. Burke, Cathy.

“L.I. charity chief convicted of embezzling nearly $1 million meant for disabled”. nydailynews.com. Retrieved 2021-04-22.

“Charitable Contributions: For use in preparing 2016 Returns” (PDF). “Scam Watch – Nigerian Scams”. Scam Watch – Australian Government. 12 May 2016. Jamie Doward (2008-03-09).

“How boom in rogue ticket websites fleeces Britons”. The Observer. London. Retrieved 9 March 2008.

“USOC and IOC file lawsuit against fraudulent ticket seller”. Sports City. Retrieved 1 August 2008. Jacquelin Magnay (4 August 2008).

“Ticket swindle leaves trail of losers”. The Sydney Morning Herald. Kelly Burke (6 August 2008). “British fraud ran Beijing ticket scam”. The Sydney Morning Herald. Francis, Ryan (2017-05-11).

“What not to get Mom for Mother’s Day”. CSO from IDG. Retrieved 2017-11-28. Hew, Khe Foon (March 2011). “Students’ and teachers’ use of Facebook”. Computers in Human Behavior. 27 (2): 662–676. doi:10.1016/j.chb.2010.11.020. Kugler, Logan (27 October 2014). “Keeping online reviews honest”. Communications of the ACM. 57 (11): 20–23. doi:10.1145/2667111. S2CID11898299. Wilson, Brian (Mar 2017). “Using Social Media to Fight Fraud”. Risk Management. New York. 64 (2): 10–11.

ProQuest1881388527. “Woman loses £320,000 in ‘romance fraud’ scam”. BBC News. Retrieved 20 October 2020. Tom Zeller Jr (April 26, 2005).

“A Common Currency for Online Fraud: Forgers of U.S. Postal Money Orders Grow”. New York Times.

“Counterfeit Money Orders: The Ultimate Guide”. Fraud Guides. 2017-09-07. Retrieved 2021-04-22.

“CyberCops.com – Counterfeit Postal Money Orders”. http://www.cybercops.com. Retrieved 23 May 2017.

“Online Shopping Scams / Scams and Fraud / Consumer Resources / Home – Florida Department of Agriculture & Consumer Services

The Future Is Looking Up for Small Businesses But Hiring Struggles Continue

A shortage of workers remains a big concern for business owners, and there’s no clear evidence yet that the end of federal unemployment benefits is boosting the labor supply

A lot has changed since unemployment reached a record rate of 14.8 percent in April 2020. Job openings are at their highest number since 2000 — and businesses can’t seem to fill them fast enough.

After any number of pandemic-related setbacks, small businesses are once again optimistic about the near future. Nearly three-fourths expect to increase sales in the next six months — but hiring struggles are putting a damper on these prospects, according to a survey of 500 small-to-medium-size businesses conducted in August 2021 and released yesterday by PNC.

Labor availability is the most-cited concern, and of the those experiencing hiring difficulties, 58 percent point to enhanced federal unemployment benefits as the culprit. With expanded federal unemployment benefits having ended on Labor Day — reducing unemployment pay by $300 a week — businesses widely believed this cut-off would lead to a surge in job applicants.

But the expected surge hasn’t yet materialized. A study released in late August authored by economists Kyle Coombs of Columbia University, Arindrajit Dube of the University of Massachusetts Amherst, and others, showed that in the 22 states that ended these federal employment benefits earlier in June, there was only a small rise in employment in subsequent months — 4.4 percent.

Small businesses are now addressing the labor shortage directly by improving pay and benefits. Of those businesses surveyed, more than four in 10 say they’ve increased compensation to help attract and retain talent, and 44 percent have started allowing more flexible work arrangements. Nearly half have also begun implementing improved health and safety measures.

These changes don’t come without a cost. More than half (54 percent) of business owners surveyed say they anticipate raising prices to compensate for increased labor costs and inflation. Once this cost is passed on to consumers, individuals who previously received federal unemployment benefits may, at last, feel increasing financial pressure to re-enter the job market.

By Rebecca Deczynski, Staff reporter, Inc.@rebecca_decz

Source: The Future Is Looking Up for Small Businesses — But Hiring Struggles Continue | Inc.com

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Related Contents:

Netflix And Boeing Among Today’s Trending Stocks

According to a report from the Washington Post dropped June 12, 1-year inflation is up 5%, while 2-year inflation sits around 5.6%. This has impacted everything from raw materials like lumber and glass to manufactured products. Used cars are up 29.7% in the last year, while gas has shot up over 56%, and washing machines and dryers sit up around 26.5%.

This comes as the global microchip shortage compounds retailers’ problems as they struggle to automate their supply chains. And while the economy (and the stock market) is certainly rebounding from covid-era recession pressures, consumers are stuck footing high-priced bills as both demand and the cost of materials continue to rise. Still, the Fed maintains that prices should stabilize soon – though “soon” may mean anywhere from 18-24 months, according to consulting firm Kearney.

Until then, investors will have to weigh their worries about inflation on the equities and bonds markets against the growing economy to decide which investments have potential – and which will see their returns gouged by rising prices across the board. To that end, we present you with Q.ai’s top trending picks heading into the new week.

Q.ai runs daily factor models to get the most up-to-date reading on stocks and ETFs. Our deep-learning algorithms use Artificial Intelligence (AI) technology to provide an in-depth, intelligence-based look at a company – so you don’t have to do the digging yourself.

Netflix, Inc (NFLX)

First up on our trending list is Netflix, Inc, which closed at $488.77 per share Friday. This represented an increase of 0.31% for the day, though it brought the streaming giant to down 9.6% for the year. The company has experienced continual losses for the past few weeks, with Friday ending below the 22-day price average of $494 and change. Currently, Netflix is trading at 47.1x forward earnings.

Netflix, Inc. trended in the latter half of last week as the company opened a new e-commerce site for branded merchandise. Currently, the store’s offerings are limited to a few popular Netflix tv shows, but the company hopes to increase its branded merchandise branded to shows such as Lupin, Yasuke, Stranger Things, and more in the coming months. With this latest move, the company hopes to expand its revenue channels and compete more directly with competitors such as Disney+.

In the last fiscal year, Netflix saw revenue growth of 5.6% to $25 billion compared to $15.8 billion three years ago. At the same time, operating income jumped 21.8% to $4.585 billion from $1.6 billion three years ago. And per-share earnings jumped almost 36% to $6.08 compared to $2.68 in the 36-month-ago period, while ROE rose to 29.6%.

Currently, Netflix is expected to see 12-month revenue around 3.33%. Our AI rates the streaming behemoth A in Growth, B in Quality Value and Low Volatility Momentum, and D in Technicals.

The Boeing Company (BA)

The Boeing Company closed down 0.43% Friday to $247.28, trending at 9.93 million trades on the day. Boeing has fallen somewhat from its 10-day price average of $250.67, though it’s up over the 22-day average of $240 and change. Currently, Boeing is up 15.5% YTD and is trading at 180.1x forward earnings.

The Boeing Company has trended frequently in recent weeks as the airplane manufacturer continues to take new orders for its jets, including the oft-beleaguered 737 MAX. United Airlines is reportedly in talks to buy “hundreds” of Boeing jets in the next few months, while Southwest Airlines is seeking up to 500 new aircraft as it expands its U.S. service. Alaskan Airlines, Dubai Aerospace Enterprise, and Ryanair have also placed orders for more Boeing jets heading into summer.

Over the last three fiscal years, Boeing’s revenue has plummeted from $101 billion to $58.2 billion, while operating income has been slashed from $11.8 billion to $8.66 billion. At the same time, per-share earnings have actually grown from $17.85 to $20.88.

Boeing is expected to see 12-month revenue growth around 7.5%. Our AI rates the airline manufacturer B in Technicals, C in Growth, and F in Low Volatility Momentum and Quality Value.

Nvidia Corporation (NVDA)

Nvidia Corporation jumped up 2.3% Friday to $713 per share, trending with 10.4 million trades on the books. Despite its sky-high stock price, Nividia has risen considerably from the 22-day price average of $631.79 – up 36.5% for the year. Currently, Nvidia is trading at 44.44x forward earnings.

Nvidia is trending this week thanks to surging GPU sales amidst the global chip shortage, as well as its planned 4-for-1 stock split at the end of June – but that’s not all. The company also announced Thursday that it also plans to buy DeepMap, an autonomous-vehicle mapping startup, for an as-yet undisclosed price. With this new acquisition, Nvidia will improve the mapping and localization functions of its software-defined self-driving operations system, NVIDIA DRIVE.

In the last fiscal year, Nvidia saw revenue growth of 15.5% to $16.7 billion compared to $11.7 billion three years ago. Operating income jumped 20.8% in the same period to $4.7 billion against $3.8 billion in the three-year ago period, and per-share earnings expanded 22.6% to $6.90. However, ROE was slashed from 49.3% to 29.8% in the same time frame.

Currently, Nvidia is expected to see 12-month revenue growth around 2%. Our AI rates Nvidia A in Growth, B in Low Volatility Momentum, C in Quality Value, and F in Technicals.

Nike, Inc (NKE)

Nike, Inc closed up 0.73% Friday to $131.94 per share, closing out the day at 5.4 million shares. The stock is down 6.7% YTD, though it’s still trading at 36.8x forward earnings.

Nike stock has slipped in recent weeks as the athleticwear retailer suffers supply chain challenges in North America. And despite recent revenue growth in its Asian markets, it also continues to deal with Chinese backlash to its March criticism of the Chinese government’s forced labor of persecuted Uyghurs.

In the last fiscal year, Nike saw revenue grow almost 3% to $37.4 billion, up 5.8% in the last three years from $36.4 billion. Operating income jumped 40.9% in the last year alone to $3.1 billion – though this is down from $4.45 billion three years ago. In the same periods, per-share earnings grew 33.7% and 82.8%, respectively, from $1.17 to $1.60. And return on equity nearly doubled from 17% to 30%.

Currently, Nike is expected to see 12-month revenue growth around 10.3%. Our AI rates Nike average across the board, with C’s in Technicals, Growth, Low Volatility Momentum, and Quality Value.

Mastercard, Inc (MA)

Mastercard, Inc ticked up 0.33% Friday to $365.50, trading at a volume of 2.7 million shares on the day. The stock is up marginally over the 22-day price average of $363.86 and 2.4% for the year. Currently, Mastercard is trading at 43.64x forward earnings.

Mastercard has faltered behind the S&P 500 index for much of the year – not to mention competitors like American Express. While there’s no one story to tie the credit card company’s relatively modest stock prices to, it may be due to a combination of investor uneasiness, already-high share prices, and increased digital payments. But with travel recently on the rise, it’s possible that Mastercard will be making a comeback.

In the last three fiscal years, Mastercard’s revenue has risen 3.3% to $15.3 billion compared to $14.95 billion. In the same period, operating income has fallen from $8.4 billion to $8.2 billion, whereas per-share earnings have grown from $5.60 to $6.37 for total growth of 16.4%. Return on equity slipped from 106% to 102.5% at the same time.

Currently, Mastercard’s forward 12-month revenue is expected to grow around 4.7%. Our deep-learning algorithms rate Mastercard, Inc. B in Low Volatility Momentum and Quality Value, C in Growth, and D in Technicals.

Q.ai, a Forbes Company, formerly known as Quantalytics and Quantamize, uses advanced forms of quantitative techniques and artificial intelligence to generate investment

Source: Netflix And Boeing Among Today’s Trending Stocks

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Critics:
The S&P 500 stock market index, maintained by S&P Dow Jones Indices, comprises 505 common stocks issued by 500 large-cap companies and traded on American stock exchanges (including the 30 companies that compose the Dow Jones Industrial Average), and covers about 80 percent of the American equity market by capitalization.
The index is weighted by free-float market capitalization, so more valuable companies account for relatively more of the index. The index constituents and the constituent weights are updated regularly using rules published by S&P Dow Jones Indices. Although called the S&P 500, the index contains 505 stocks because it includes two share classes of stock from 5 of its component companies.

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References:

How The Power Of Predictive Analytics Can Transform Business

Tableau analytics visual

With the acceleration of digital transformation in business, most CTOs, CIOs, and even middle management or analysts are now asking, “What’s next with data?” and what ongoing role will technology play in both digital and data transformations. Other questions that keep these individuals up at night include:

  • How can people throughout all organizational levels be more empowered to use data and help others make better decisions?
  • What prevents people from more deeply exploring and using data?
  • In what ways can analytics tools and methods help more people use data in the daily routine of business—asking questions, exploring hypotheses, and testing ideas?

With this in mind, plus observations and discussions with many Tableau customers and partners, it seems that today’s circumstances, behaviors, and needs make it the right time for predictive data analytics to help businesses and their people solve problems effectively.

Current realities and barriers to scale smarter decision-making with AI 

With growing, diverse data sets being collected, the analytics use cases to transform data into valuable insights are growing just as fast. Today, a wide range of tools and focused teams specialize in uncovering data insights to inform decision-making, but where organizations struggle is striking the right balance between activating highly technical data experts and business teams with deep domain experience.

Until now, using artificial intelligence (AI), machine learning (ML), and other statistical methods to solve business problems was mostly the domain of data scientists. Many organizations have small data science teams focused on specific, mission-critical, and highly scalable problems, but those teams usually have a long project list to handle.

At the same time though, there are a large number of business decisions that rely on experience, knowledge, and data—and that would greatly benefit from applying more advanced analysis techniques. People with domain knowledge and proximity to the business data could benefit greatly, if they had access to these techniques.

Instead, there’s currently a back-and-forth process of relying on data scientists and ML practitioners to build and deploy custom models—a cycle that lacks agility and the ability to iterate quickly. By the end, the data that the model was trained on could be stale and the process starts again. But organizations depend on business users to make key decisions daily that don’t rise to the priority level of their central data science team.

The opportunity to solve data science challenges

This is where there’s an opportunity to democratize data science capabilities, minimizing the trade-offs between extreme precision and control versus the time to insight—and the ability to take action on these insights. If we can give people tools or enhanced features to better apply predictive analytics techniques to business problems, data scientists can gain time back to focus on more complex problems. With this approach, business leaders can enable more teams to make data-driven decisions while continuing to keep up with the pace of business. Additional benefits gained from democratizing data science in this way include:

  • Reducing data exploration and prep work
  • Empowering analyst experts to deliver data science outputs at lower costs
  • Increasing the likelihood of producing successful models with more exploration of use cases by domain experts
  • Extending, automating, and accelerating analysis for business groups and domain experts
  • Reducing time and costs spent on deploying and integrating models
  • Promoting responsible use of data and AI with improved transparency and receiving guidance on how to minimize or address bias

Business scenarios that benefit from predictive analytics 

There are several business scenarios where predictive capabilities can be immensely useful.

Sales and marketing departments can apply it to lead scoring, opportunity scoring, predicting time to close, and many other CRM-related cases. Manufacturers and retailers can use it to help with supply chain distribution and optimization, forecasting consumer demand, and exploring adding new products to their mix. Human resources can use it to assess the likelihood of candidates accepting an offer, and how they can adjust salary and benefits to meet a candidate’s values. And companies can use it to explore office space options and costs. These are just a few of the potential scenarios.

A solution to consider: Tableau Business Science

We are only at the beginning of exploring what predictive capabilities in the hands of people closely aligned with the business will unlock. AI and ML will continue to advance. More organizations, in a similar focus as Tableau, will also keep looking for techniques that can help people closest to the business see, understand, and use data in new ways to ask and answer questions, uncover insights, solve problems, and take action.

This spring Tableau introduced a new class of AI-powered analytics that gives predictive capabilities to people who are close to the business. In this next stage of expanded data exploration and use, we hope business leaders embrace data to help others make better decisions, and to provide transparent insight into the factors influencing those decisions.

When people can think with their data—when analysis is more about asking and answering questions than learning complex software or skills—that’s when human potential will be unleashed, leading to amazing outcomes. Learn more about Tableau Business Science, what this technology gives business teams, and the value it delivers to existing workflows.

Olivia Nix is a Senior Manager of Product Marketing at Tableau. She leads a team focused on the use of AI and ML in analytics and engagement, including how to use technology to enable more people in organizations to make data-driven decisions. Olivia has been at Tableau for four years where she has worked closely with development teams on new product launches. Prior to Tableau, Olivia worked as an analyst at the Pew Center on Global Climate Change (now C2ES) and Johnson Controls. She has her MBA from the UCLA Anderson School of Management.

Source: How The Power Of Predictive Analytics Can Transform Business

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Critics:

Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions.

The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement.

Predictive analytics is used in actuarial science,marketing,business management, sports/fantasy sports, insurance,policing, telecommunications,retail, travel, mobility, healthcare, child protection, pharmaceuticals,capacity planning, social networking and other fields.

One of the best-known applications is credit scoring,[1] which is used throughout business management. Scoring models process a customer’s credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time.

Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. The enhancement of predictive web analytics calculates statistical probabilities of future events online. Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining.Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.

For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs.The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.

Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, “Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.”In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization.

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