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Fraud Attacks Often Come From Unexpected Places – Can Predictive Analytics Help?

Jerome Pugnet

Looking at companies’ experiences, of various sizes and across all industries, I think we would all agree that fraud attacks often don’t come from where one would expect! Companies still rely too much on guesswork and empiric methods while investigating potentially fraudulent transactions.

And to make things worse, fraud patterns evolve quickly and constantly. Thus, as companies put in place measures to prevent fraud, perpetrators quickly adapt and find ways to circumvent them. There’s clearly a need for better processes and tools to enhance their fraud detection and investigation.

Investigators’ experience isn’t sufficient anymore

To analyse and understand how and where fraud happens, one can’t just rely on the experience and intuitions of even the best investigators, or the analysis of standard fraud reports and basic metrics. Also, the more common analytical tools appear ineffective to scan very high and fast-growing volumes of data – where critical information to understand fraud patterns and hidden paths is buried.

Moreover, the range of data to examine to properly identify fraud trends is increasingly diverse – structured and unstructured. More than ever, fraud detection is a Big Data problem!

Fast-developing predictive technologies offer great potential for improvement

On the other hand, predictive analysis technologies are fast developing, becoming more widely available and easier to use, yet more powerful. They can help companies get deep insights into how and where fraudulent transactions originate, and analyze changing fraud patterns, in order to enhance their fraud detection strategies and adapt faster to new types of attacks.

So the combination of traditional fraud management solutions complemented by predictive analytics not only enhances capabilities to detect fraud, but also contributes to better prevention of potential future fraud. It enables a deeper, more forensic approach against fraud, helping users to improve the effectiveness of their investigations by better focusing on new types of fraud risks, and continuously updating and refining their fraud detection strategies using the data from predictive analyses.

Today’s best fraud management and predictive analytics solutions have many benefits. They:

  • Identify fraud patterns and trends more precisely: where fraud comes from, how it happens, who is involved, what areas of the business it impacts, and so on.
  • Enable going after the less known and more complex patterns and networks, and detecting earlier to minimize the damage of cleverly hidden suspicious transactions.
  • Provide the needed capabilities to analyze a wide variety and very high volume of data very fast, leveraging in-memory computing technology.
  • Help fraud investigators by reducing false alerts resulting from inadequate fraud detection mechanisms— a critical issue today for many fraud investigators as they’re faced with an excessive workload of potential alerts to analyse, and wasted efforts as many turn out to be false positives.

Can predictive analytics benefit a wider audience?

The innovation brought by predictive analytics touches many other areas of the business, and in areas such as governance, risk and compliance (GRC), its use will develop to enable better predictability of risk, increased insight in areas of control weakness, support for internal audit programs, and so on.

These multiple applications create a high demand for experts such as data analysts and specialized business analysts, but the scarcity and high cost of these resources pushes for better usability of the tools. In the area of fraud in particular, invaluable expertise resides within fraud investigation teams who don’t have these skills as their primary asset.

For them, and others, it’s important that new predictive technologies become approachable for the non-experts, and more readily consumable by their most interested audience—which is just what the latest generations of predictive technologies enable.

For more on security strategies, see Cybersecurity: Is It Time To Change Our Mindset?

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Smart Machines Create Markets For Cyber-Physical Advances

Marion Heindenreich

Today, industrial machines are more intelligent than ever before. These intelligent machines are changing companies in many ways.

Why smart machines?

Mobile networked computers were a key breakthrough for making smart machines. Big Data allows machines and computers to store information and analyze complex patterns. Cloud computing offers broad access to information and more storage.

These computerized machines are both physical and virtual. Some call them “cyber-physical” machines. Technology lets them be self-aware and connected to each other and larger systems.

Businesses change their approaches

Intelligent machines allow companies to innovate in many areas. For one, the value proposition for customers is evolving. Businesses now model and plan in different ways in many industries.

Makers of industrial machines and parts work in new ways within the organization. Engineering now partners with mechanical, electronic, and software staff to develop new products. Manufacturing now seamlessly ties what happens on the shop floor to the customer.

Service models are changing too. Scheduled and reactionary servicing of machines is fading. Now intelligent machines track themselves. Machines detect problems and report them automatically. Major problems or failures are predicted and reported.

A data mining example

One good industrial example is mining, which can be dangerous and difficult. As ores become scarce, the costs of mining have increased.

“Smart machines” started in mining in the late 1990s. Software and hardware let remote users change settings. Operators moved hydraulic levers from a safe distance. Sensors observed performance and diagnosed issues.

Data cables connected machines to computers on the surface. Continuous and remote monitoring of the machines grew. Over time, embedded sensors helped improve monitoring, diagnostics, and data storage.

The technology means workers only go underground to fix specific issues. As a result, accident and injury risk is lower.

New wireless technology now lets mining companies connect data from many mine sites. Service centers access large amounts of data and can improve performance. Maintenance is prioritized and equipment downtime is reduced.

Opportunity abounds

For companies the time is now. Today, mobile “connected things” generate 17% of the digital universe. By 2020 that share grows to 27%.

You might not be investing in this so-called “Internet of Things” (devices that connect to each other). But it’s a good bet your competitors are. A December 2015 study reported 33% of industrial companies are investing in the Internet of Things. Another 25% are considering it.

There are risks

This new dawning era of manufacturing is exciting. But there are concerns. Cyber attacks on the Internet of Things are not new. But as the use of intelligent machines grows, the threat of cyber attacks in industry grows.

Data confidentiality and privacy are concerns. So too are software and hardware vulnerabilities. Exposure to attack lies not just in the virtual space but the physical too. Tampering with unattended machines and theft pose serious risk.

To address these threats, industries must invest in cybersecurity along with smart machines.

Conclusion

The potential advantages of smart machines are staggering. They can reshape industries and change how companies produce new products and create new markets.

For more information, please download the white paper Digital Manufacturing: Powering the Fourth Industrial Revolution.

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Marion Heindenreich

About Marion Heindenreich

Marion Heidenreich is a solution manager for the SAP Industrial Machinery and Components Business Unit who focuses on solution innovations like Product Costing on SAP HANA and cloud solutions, as well as providing financial and business analysis for industry business strategy definition and business planning.

Mining Firms Turn To Tech

Ruediger Schroedter

Gone are the days in mining when assessments of potential dig sites meant lots of waiting for results. Gone, too, is the uncertainty on a mine job about where to go next.

For mining executives, recent advances in digital technology allow companies to make decisions at a rapid pace. Decisions that used to take days and weeks now can be done in minutes and hours.

With more information available faster, mining leaders reduce both short- and long-term financial risk. Data from across the enterprise inform decisions about buying and selling assets. Profitability should increase, driven by key technology advances.

Digging in to the data

There are two key drivers to this digital revolution. The first is the rise of the Internet of Things (IoT). The IoT consists of devices that are equipped with sensors, software, and wireless capabilities. These devices are connected to each other and can detect, store, and send data.

Bonus: Click here to learn more about Digital Transformation in Mining.

The second is the rise of Big Data, mobile, and cloud computing. Today’s mobile devices can track, send, and receive data from remote sites worldwide. Cloud computing stores billions of bytes of data at low cost. Big Data analytics programs take data coming from many different locations and systems and synthesize it. Those programs then better inform decisions by offering dashboards, metrics, and predictive modeling.

Robots are able to venture into hazardous areas and move material with remote human oversight. On-site mining data is sent via mobile phone to a cloud-based platform. For mining, the convergence of these technologies provides extraordinary possibilities.

Technology at play

The potential impact is significant. A recent report by McKinsey & Co. showed the use of advanced analytics in mining and related industries had a major impact. Firms using these programs to assess production areas increased their profit margins by 2-3 percentage points.

One mining company used so-called Monte Carlo simulations to reduce certain capital expenses. Monte Carlo simulations use complex algorithms and repeated random sampling to model possible outcomes. They’re frequently used in finance, biology, and insurance. The Mining Journal reported how the company challenged assumptions about a project’s capital needs. It took historical data on certain disruptions such as rainfall patterns. Then models of its mines were made showing the impact of flooding and rainwater. The data led to a new strategy that maximized storage capacity and handling across all its mines. Capital costs dropped by 20 percent.

18 Aug 2012, South Dakota, USA --- USA, South Dakota, Lead, View of open pit --- Image by © Bryan Mullennix/Tetra Images/Corbis

Buy or sell?

With so many variables at play, mining valuation is not for the faint of heart. Integrated data streams available at the discovery stage make for better informed purchase decisions.

Software programs today can take data to build and validate exploration models. These programs use 3D visualization and validated geophysical, analytical, and drill hole data. In turn, detailed 3D topographical models are possible.

Other programs assess historical, assay, and drilling data. This information creates viable scenarios for determining whether to buy or sell a site.

These tools use data consistently from one potential site to the next, allowing for forecasting of economic risk that is consistent across the organization. The firm today can use “real options valuation” to develop models of outcomes given changing economic conditions. With clearer information about potential risks, firms can decide whether to stage, sell, abandon, expand, or buy.

Anticipating, not reacting

Mining companies realize today that these analytic platforms and dashboards offer many advantages. Users have a clearer interpretation of the aggregated and analyzed data points from multiple areas. Using predictive analytics, mining decisions are made based on smart assumptions, not past historical information.

Robust software programs can generate reports almost instantaneously. Supervisors have on-site access to the analysis through a web browser or app. This data has many uses. Drilling managers save time and can make quicker decisions on next moves. Supplies can be ordered faster. Needed data for accreditation and compliance is immediately accessible.

Selecting the right sites

One example is assay analysis. Today, geologists do not wait weeks or months for assay results. Instead of off-site analysis, web-based applications deliver information much faster to inform decisions.

Robots are sending information about field operations, safety, needed maintenance, and drilling performance.  Some devices send the information themselves. In other cases, staff use mobile phones, tablets, or laptops.  This information and analytics in turn help with site selection. Integrating data from mine planning, ventilation, safety, rock engineering, and mineral resources improves overall forecasting.

Discovery, particularly of Tier 1 sites, is an increasingly costly venture for mining companies. Demand for many products is increasing while discovery rates are dropping. Mined product is of a lesser quality, particularly in mature mining locations. Many possible sites are in areas that are underexplored areas with difficult and deep cover.

The advanced technologies available today are contributing to rapid improvement in these discovery issues.

Prospective drilling

Consider the drill hole. To reduce costs in exploration, there needs to be enough rich information from the opening drill hole. It needs to be delivered in as close to real time as possible. Doing so lessens the risk of the second drill hole. Better information from the start helps improve vectoring. It provides better information about what mineral systems are being drilled.

This approach, called prospective drilling, is becoming increasingly used in mining. It employs drilling activity to map covered mineral systems. In turn, geochemical and geophysical vectoring can lead firms toward deposits.

Australia has invested heavily in this area. The Deep Exploration Technologies Cooperative Research Centre (DET CRC) has a singular vision: uncovering the future. Its core purpose is “develop transformational technologies for successful mineral exploration through deep, barren cover rocks.”

To get to that point, the DET CRC is borrowing a drilling technique from the oil business. Coiled tubing is paired with downhole and top-of-the-hole sensors. The informaton provides petrophysical, structural, rock fabric, geochemical, and mineralogical data all at once.

Conclusion

To meet increasing demands for new viable sites, and to improve efficient on sites, mining is changing. Using smart, connected products and robust data modeling, mining is being done faster, safer, and more efficiently than ever.

Join a LiveTwitterChat on digitalization in mining on May 4th from 10-11 a.m. EST: #digitalmining

The global mining and metals industry will come together to discuss how digital innovation is impacting the mining industry July 12-14 at the International SAP Conference for Mining and Metals in Frankfurt, Germany.  Don’t miss this opportunity to meet with world leaders and learn how your organization can become a connected digital enterprise.

Follow speakers and pre-event activities by following sapmmconf and @sapmillmining on Twitter

AA Mining and Metals Forum

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Ruediger Schroedter

About Ruediger Schroedter

Ruediger Schroedter is responsible for solution management of SAP solutions for the mining industry worldwide. He has spent more than 15 years in the mill products and mining industries and has extensive experience implementing SAP solutions for customers in these industries before coming to SAP.

How Much Will Digital Cannibalization Eat into Your Business?

Fawn Fitter

Former Cisco CEO John Chambers predicts that 40% of companies will crumble when they fail to complete a successful digital transformation.

These legacy companies may be trying to keep up with insurgent companies that are introducing disruptive technologies, but they’re being held back by the ease of doing business the way they always have – or by how vehemently their customers object to change.

Most organizations today know that they have to embrace innovation. The question is whether they can put a digital business model in place without damaging their existing business so badly that they don’t survive the transition. We gathered a panel of experts to discuss the fine line between disruption and destruction.

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qa_qIn 2011, when Netflix hiked prices and tried to split its streaming and DVD-bymail services, it lost 3.25% of its customer base and 75% of its market capitalization.²︐³ What can we learn from that?

Scott Anthony: That debacle shows that sometimes you can get ahead of your customers. The key is to manage things at the pace of the market, not at your internal speed. You need to know what your customers are looking for and what they’re willing to tolerate. Sometimes companies forget what their customers want and care about, and they try to push things on them before they’re ready.

R. “Ray” Wang: You need to be able to split your traditional business and your growth business so that you can focus on big shifts instead of moving the needle 2%. Netflix was responding to its customers – by deciding not to define its brand too narrowly.

qa_qDoes disruption always involve cannibalizing your own business?

Wang: You can’t design new experiences in existing systems. But you have to make sure you manage the revenue stream on the way down in the old business model while managing the growth of the new one.

Merijn Helle: Traditional brick-and-mortar stores are putting a lot of capital into digital initiatives that aren’t paying enough back yet in the form of online sales, and they’re cannibalizing their profits so they can deliver a single authentic experience. Customers don’t see channels, they see brands; and they want to interact with brands seamlessly in real time, regardless of channel or format.

Lars Bastian: In manufacturing, new technologies aren’t about disrupting your business model as much as they are about expanding it. Think about predictive maintenance, the ability to warn customers when the product they’ve purchased will need service. You’re not going to lose customers by introducing new processes. You have to add these digitized services to remain competitive.

qa_qIs cannibalizing your own business better or worse than losing market share to a more innovative competitor?

Michael Liebhold: You have to create that digital business and mandate it to grow. If you cannibalize the existing business, that’s just the price you have to pay.

Wang: Companies that cannibalize their own businesses are the ones that survive. If you don’t do it, someone else will. What we’re really talking about is “Why do you exist? Why does anyone want to buy from you?”

Anthony: I’m not sure that’s the right question. The fundamental question is what you’re using disruption to do. How do you use it to strengthen what you’re doing today, and what new things does it enable? I think you can get so consumed with all the changes that reconfigure what you’re doing today that you do only that. And if you do only that, your business becomes smaller, less significant, and less interesting.

qa_qSo how should companies think about smart disruption?

Anthony: Leaders have to reconfigure today and imagine tomorrow at the same time. It’s not either/or. Every disruptive threat has an equal, if not greater, opportunity. When disruption strikes, it’s a mistake only to feel the threat to your legacy business. It’s an opportunity to expand into a different marke.

SAP_Disruption_QA_images2400x1600_4Liebhold: It starts at the top. You can’t ask a CEO for an eight-figure budget to upgrade a cloud analytics system if the C-suite doesn’t understand the power of integrating data from across all the legacy systems. So the first task is to educate the senior team so it can approve the budgets.

Scott Underwood: Some of the most interesting questions are internal organizational questions, keeping people from feeling that their livelihoods are in danger or introducing ways to keep them engaged.

Leon Segal: Absolutely. If you want to enter a new market or introduce a new product, there’s a whole chain of stakeholders – including your own employees and the distribution chain. Their experiences are also new. Once you start looking for things that affect their experience, you can’t help doing it. You walk around the office and say, “That doesn’t look right, they don’t look happy. Maybe we should change that around.”

Fawn Fitter is a freelance writer specializing in business and technology. 

To learn more about how to disrupt your business without destroying it, read the in-depth report Digital Disruption: When to Cook the Golden Goose.

Download the PDF (1.2MB)

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Digitization Of The Supplier Network: Grinding Away Competitive Edges

Kai Goerlich

Competitors with advanced digital capabilities are invading markets with new disruptive business models – and a range of new challenges across all industries. Prices are falling and changing quickly. Margins are thinning. Resources are increasingly volatile while the balance between supply and fast-changing customer demands are next-to-impossible to match. All the while, 30% of industry leaders are at risk of being disrupted by 2018 by a digitally enabled competitor, according to IDC.

Under these conditions, companies are beginning to ask whether their supply networks should be open to the digital world. Will they accept the risk of being copied and losing competitive advantage? Or will they secure their best practices in supply chain and logistics?

Using an analytical framework of 15 ecosystem factors, we compared traditional companies against digital newcomers. Our ad hoc study revealed that digitization influences business systems on several levels, but standard best practices are not one of them.

Network resiliency

In most supply chains, the hierarchical model is still living and prospering. Digital newcomers usually create a web-like structure across the entire business. While the traditional approach may guarantee price stability and quality, this web structure allows a much faster ramp-up and exchange of partners – making it more resilient to change.

Dependencies

In traditional networks, the business is likely evolving around mutual advantages. Very often, there are tight, symbiotic business connections with limited sets of partners. New digital networks are operating with an increased focus on leveraging opportunities. Plus, partners are encouraged to participate, widen, and promote the network – even if they do not directly contribute to revenue or profit margins.

Brand management

Web structures are especially attractive to companies that find it difficult to access traditional value chains. In general, classic supply chains cannot keep up with the speed of change nor deal with new and unexpected supply-chain partners in future digital networks. And as “new and unexpected” translate into “interesting and exciting” for consumers, companies may encounter significant branding issues.

Path dependency

Digital newcomers usually have a lower path dependency, such as mode of action. Unfortunately, this can be attributed to perspectives and business plans that are not based on decades of experience in one business. Of course, knowing a business for many years has its advantages as well – but only if knowledge is successfully transferred into the digital world.

A new way to operate

As pointed out in an earlier blog, digitization is proven to be a shortcut for some traditional processes and functions. In turn, embedding best practices into supply-chain and logistics processes and avoiding any transfer of knowledge as long as possible may appear to be an obvious solution. However, according to our findings, it might not be the best path to dealing with changes related to digital transformation.

While digitization may indeed wash away former competitive advantages, it also empowers companies to use their vast knowledge and connections to get on par with digital newcomers – on a new and different level. For example, most traditional best practices are now outsourced and can be easily applied as a service. But more important, instead of waiting to be disrupted by digitization, businesses can become as flexible as possible to enhance the customer experience and build loyalty.

For more on disruption without damage, see 4 Ways to Digitally Disrupt Your Business Without Destroying It.

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Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Idea Director of Thought Leadership at SAP. His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation.