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The Power of Algorithmic Forecasting

This is the first in a series of articles by Boston Consulting Group and Daimler Mobility discussing the concept of forward-looking financial steering. Here, we introduce the concept and explain how companies can use it. Subsequent articles will address implementation challenges related to people and technology. The insights are derived from Daimler Mobility’s successful deployment, with BCG’s support, of forward-looking steering in its global operations.

People don’t steer their cars solely on the basis of what they see in the rearview mirror, yet that is essentially how most business leaders steer their companies: they look backward to decide how to move forward. This method makes it hard for companies to cope with the ever-increasing levels of uncertainty in today’s business environment. To keep up to speed, companies need an approach to financial steering that permits rapid and effective course corrections in anticipation of future developments. Companies should spend far less time developing detailed plans and far more time taking action to counter threats and capture opportunities.

To make that happen, the paradigm for steering must fully shift its focus from backward looking to forward looking. Backward-­looking steering entails analyzing deviations between plan targets and actual performance. Forward-­looking steering entails comparing targets with forecasts of how KPIs will evolve over specific time horizons. To truly adopt forward-looking steering (as described in this article), a company must use algorithmically derived forecasts.

Although it is common for companies to produce forecasts manually, few companies use algorithms. Algorithmically derived forecasts allow the focus to shift from periodically reporting results to accurately forecasting the development of KPIs—faster and with less effort. Armed with foresight into how conditions will change, companies can take action to preempt unfavorable outcomes and promote competitive advantage.

Adopting algorithm-based, forward-looking steering is not easy, however. A company must enrich its traditional manual processes with a data-driven, automated approach to generating forecasts and performance reports. Among the many challenges are assembling a team that has statistical capabilities, setting up a new technical infrastructure, and building people’s trust in technology.

“To master the digital transformation, a company must take a comprehensive approach to algorithm-based forward-looking steering,” says Stephan Unger, Daimler Mobility’s Chief Financial Officer (CFO). “This includes not only advanced analytical methods, new technologies, and the right expertise, but also an engaging approach to change management.”

By Gerhard Unger and Marc Rodt

More: https://www.bcg.com/publications/

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When women lead, workplaces should listen

For years, female executives have come away from women-only leadership programs empowered to do—and ask for—more, valuing the opportunity to examine their strengths and shortcomings in the psychological safety of their peers and to use the experience as a springboard for personal development.

But organizations are leaving unexamined the most powerful lessons these programs offer.

The oft-overlooked benefit of women-only leadership programs is that they hold up a mirror to the organization. When women scrutinize their own leadership traits and experiences, they reveal important information about the day-to-day environment in which they operate. If a company is receptive, the content of the sessions can help gauge how well the organization promotes effective leadership behavior and can offer a portal into where the company succeeds, as well as where it fails to foster an environment in which everyone can bring their best self to work. In short, companies can use such programs not only to improve the skills of the participants but also to assess—and ultimately improve—the workplace itself.

We’ve come to these conclusions through a decade’s worth of experience in a particular women’s leadership program—McKinsey’s Remarkable Women Program, which has helped develop female leaders from Warsaw to Washington, DC, to Singapore to Stockholm. Remarkable Women sessions generally include participants from multiple organizations, but many companies send more than one woman, and we believe that the lessons we’ve learned are equally relevant for organizations running their own in-house programs.

In this article, we describe what hundreds of program sessions and 150 interviews with participants have taught us. Not only do women and men experience work differently; not only is it the system—rather than women—that needs fixing; but there are three critical actions organizations need to take: they must broaden their leadership models, stimulate dissent, and encourage more effective introspection across the board.

About the authors: Natacha Catalino is an associate partner in McKinsey’s Boston office, and Kirstan Marnane is a senior advisor in the London office.

More: https://www.mckinsey.com

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Five routes to more innovative problem solving

Rob McEwen had a problem. The chairman and chief executive officer of Canadian mining group Goldcorp knew that its Red Lake site could be a money-spinner—a mine nearby was thriving—but no one could figure out where to find high-grade ore. The terrain was inaccessible, operating costs were high, and the unionized staff had already gone on strike. In short, McEwen was lumbered with a gold mine that wasn’t a gold mine.

Then inspiration struck. Attending a conference about recent developments in IT, McEwen was smitten with the open-source revolution. Bucking fierce internal resistance, he created the Goldcorp Challenge: the company put Red Lake’s closely guarded topographic data online and offered $575,000 in prize money to anyone who could identify rich drill sites. To the astonishment of players in the mining sector, upward of 1,400 technical experts based in 50-plus countries took up the problem. The result? Two Australian teams, working together, found locations that have made Red Lake one of the world’s richest gold mines. “From a remote site, the winners were able to analyze a database and generate targets without ever visiting the property,” McEwen said. “It’s clear that this is part of the future.”

McEwen intuitively understood the value of taking a number of different approaches simultaneously to solving difficult problems. A decade later, we find that this mind-set is ever more critical: business leaders are operating in an era when forces such as technological change and the historic rebalancing of global economic activity from developed to emerging markets have made the problems increasingly complex, the tempo faster, the markets more volatile, and the stakes higher. The number of variables at play can be enormous, and free-flowing information encourages competition, placing an ever-greater premium on developing innovative, unique solutions.

This article presents an approach for doing just that. How? By using what we call flexible objects for generating novel solutions, or flexons, which provide a way of shaping difficult problems to reveal innovative solutions that would otherwise remain hidden. This approach can be useful in a wide range of situations and at any level of analysis, from individuals to groups to organizations to industries. To be sure, this is not a silver bullet for solving any problem whatever. But it is a fresh mechanism for representing ambiguous, complex problems in a structured way to generate better and more innovative solutions.

The flexons approach

Networks flexon

Evolutionary flexon

Decision-agent flexon

System-dynamics flexon

Information-processing flexon

Putting flexons to work

Flexons help turn chaos into order by representing ambiguous situations and predicaments as well-defined, analyzable problems of prediction and optimization. They allow us to move up and down between different levels of detail to consider situations in all their complexity. And, perhaps most important, flexons allow us to bring diversity inside the head of the problem solver, offering more opportunities to discover counterintuitive insights, innovative options, and unexpected sources of competitive advantage.

About the author(s)

Olivier Leclerc is a principal in McKinsey’s Southern California office. Mihnea Moldoveanu is associate dean of the full-time MBA program at the University of Toronto’s Rotman School of Management, where he directs the Desautels Centre for Integrative Thinking.

More: www.mckinsey.com

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Financial crime and fraud in the age of cybersecurity

As cybersecurity threats compound the risks of financial crime and fraud, institutions are crossing functional boundaries to enable collaborative resistance.

In 2018, the World Economic Forum noted that fraud and financial crime was a trillion-dollar industry, reporting that private companies spent approximately $8.2 billion on anti–money laundering (AML) controls alone in 2017. The crimes themselves, detected and undetected, have become more numerous and costly than ever. In a widely cited estimate, for every dollar of fraud institutions lose nearly three dollars, once associated costs are added to the fraud loss itself. 1 Risks for banks arise from diverse factors, including vulnerabilities to fraud and financial crime inherent in automation and digitization, massive growth in transaction volumes, and the greater integration of financial systems within countries and internationally. Cybercrime and malicious hacking have also intensified. In the domain of financial crime, meanwhile, regulators continually revise rules, increasingly to account for illegal trafficking and money laundering, and governments have ratcheted up the use of economic sanctions, targeting countries, public and private entities, and even individuals. Institutions are finding that their existing approaches to fighting such crimes cannot satisfactorily handle the many threats and burdens. For this reason, leaders are transforming their operating models to obtain a holistic view of the evolving landscape of financial crime. This view becomes the starting point of efficient and effective management of fraud risk.

The evolution of fraud and financial crime

Fraud and financial crime adapt to developments in the domains they plunder. (Most financial institutions draw a distinction between these two types of crimes: for a view on the distinction, or lack thereof, see the sidebar “Financial crime or fraud?”) With the advent of digitization and automation of financial systems, these crimes have become more electronically sophisticated and impersonal.

Financial crime or fraud?

For purposes of detection, interdiction, and prevention, many institutions draw a distinction between fraud and financial crime. Boundaries are blurring, especially since the rise of cyberthreats, which reveal the extent to which criminal activities have become more complex and interrelated. What’s more, the distinction is not based on law, and regulators sometimes view it as the result of organizational silos. Nevertheless, financial crime has generally meant money laundering and a few other criminal transgressions, including bribery and tax evasion, involving the use of financial services in support of criminal enterprises. It is most often addressed as a compliance issue, as when financial institutions avert fines with anti–money laundering activities. Fraud, on the other hand, generally designates a host of crimes, such as forgery, credit scams, and insider threats, involving deception of financial personnel or services to commit theft. Financial institutions have generally approached fraud as a loss problem, lately applying advanced analytics for detection and even real-time interdiction. As the distinction between these three categories of crime have become less relevant, financial institutions need to use many of the same tools to protect assets against all of them.

One series of crimes, the so-called Carbanak attacks beginning in 2013, well illustrates the cyber profile of much of present-day financial crime and fraud. These were malware-based bank thefts totaling more than $1 billion. The attackers, an organized criminal gang, gained access to systems through phishing and then transferred fraudulently inflated balances to their own accounts or programmed ATMs to dispense cash to waiting accomplices (Exhibit 1).

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Significantly, this crime was one simultaneous, coordinated attack against many banks. The attackers exhibited a sophisticated knowledge of the cyber environment and likely understood banking processes, controls, and even vulnerabilities arising from siloed organizations and governance. They also made use of several channels, including ATMs, credit and debit cards, and wire transfers. The attacks revealed that meaningful distinctions among cyberattacks, fraud, and financial crime are disappearing. Banks have not yet addressed these new intersections, which transgress the boundary lines most have erected between the types of crimes (Exhibit 2).

More: https://www.mckinsey.com/business-functions/risk

Authors: Salim Hasham is a partner in McKinsey’s New York office, where Shoan Joshi is a senior expert; Daniel Mikkelsen is a senior partner in the London office.

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The Company of the Future

In the coming decade, companies will increasingly need to compete on the rate of learning. Technology promises to play a critical role: artificial intelligence can detect patterns in complex data sets at extreme speed and scale, enabling dynamic learning. This will allow organizations to constantly adapt to changing realities and surface new opportunities, which will be increasingly important in an uncertain and fast-changing environment.

But for companies to compete on learning, it is not enough to merely adopt AI, which alone can accelerate learning only in individual activities. As with previous transformative technologies, unlocking the full potential of AI—and of humans—will require fundamental organizational innovation.

In other words, to win the ’20s, leaders will need to re-invent the enterprise as a next-generation learning organization.

The next-generation learning organization will need to be redesigned to fulfill several key functions: (See Exhibit 1.)

  • Learning on All Timescales. The growing opportunity and need to learn on faster timescales, driven by technological innovation, is well known—algorithmic trading, dynamic pricing, and real-time customized product recommendations are already a reality in many businesses. But it is perhaps under-appreciated that slow-moving forces are also becoming more important. For example, trade institutions, political structures, wealth stratification, and social attitudes are slowly changing in ways that could have a profound impact on business. Gone are the days when business leaders could focus only on business and treat these broader variables as constants or stable trends. But such shifts unfold over many years or even decades. In order to thrive sustainably, businesses must learn on all timescales simultaneously.
  • Combining Humans and Machines Optimally. Machines have been crucial components of businesses for centuries—but in the AI age, they will likely expand rapidly into what has traditionally been considered white-collar work. Instead of merely executing human-directed and designed processes, machines will be able to learn and adapt, and will therefore have a greatly expanded role in future organizations. Humans will still be indispensable, but their duties will be quite different when complemented or substituted by intelligent machines.
  • Integrating Economic Activity Beyond Corporate Boundaries. Businesses are increasingly acting in multicompany ecosystems that incorporate a wide variety of players. Indeed, seven of the world’s largest companies, and many of the most profitable ones, are now platform businesses. Ecosystems greatly expand learning potential: they provide access to exponentially more data, they enable rapid experimentation, and they connect with larger networks of suppliers of customers. Harnessing this potential requires redrawing the boundaries of the enterprise and effectively influencing economic activity beyond the orchestrating company.
  • Evolving the Organization Continuously. The need for dynamic learning does not apply just to customer-facing functions—it also extends to the inner workings of the enterprise. To take advantage of new information and to compete in dynamic, uncertain environments, the organizational context itself needs to be evolvable in the face of changing external conditions.
  • By reconceiving the external and internal workings of the organization as a flexible, evolving ecosystem, businesses can handle much greater dynamism and complexity. This requires subjecting all aspects of the organization to market forces, enabling it to learn and adapt in response to new opportunities. And it requires internal systems that adjust automatically to new information, allowing learning and resource reallocation to occur at algorithmic speed. When combined, these capabilities can create a “self-tuning enterprise” that constantly learns and evolves according to its environment. (See Exhibit 3.)To harness the power of ecosystems throughout and beyond the organization, leaders must:
    • Engage external partners to create a shared vision of the future.
    • Develop capabilities for collaboration and information sharing at scale—for example, platforms and APIs.
    • Redesign internal processes to be more adaptive and data-driven, allowing the organization to become “self-tuning.”

By reconceiving the external and internal workings of the organization as a flexible, evolving ecosystem, businesses can handle much greater dynamism and complexity. This requires subjecting all aspects of the organization to market forces, enabling it to learn and adapt in response to new opportunities. And it requires internal systems that adjust automatically to new information, allowing learning and resource reallocation to occur at algorithmic speed. When combined, these capabilities can create a “self-tuning enterprise” that constantly learns and evolves according to its environment. (See Exhibit 3.)

To harness the power of ecosystems throughout and beyond the organization, leaders must:

  • Engage external partners to create a shared vision of the future.
  • Develop capabilities for collaboration and information sharing at scale—for example, platforms and APIs.
  • Redesign internal processes to be more adaptive and data-driven, allowing the organization to become “self-tuning.”

By Allison Bailey, Martin Reeves, Kevin Whitaker, and Rich Hutchinson

More: BCG https://www.bcg.com/

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