Analytics

Analytics plays a vital role  especially in connection with inflation in Advanced Stage Transfinancial Economics. http://www.p2pfoundation.net/Transfinancial_Economics

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A sample Google Analytics dashboard. Tools like this help businesses identify trends and make decisions.

Analytics is the discovery and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight.
Firms may commonly apply analytics to business data, to describe, predict, and improve business performance. Specifically, arenas within analytics include enterprise decision management, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix analytics, web analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation (See Big Data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.^[1]

Contents

 [hide] 

• 1 Analytics vs. analysis
• 2 Examples
  • 2.1 Marketing optimization
  • 2.2 Portfolio analysis
  • 2.3 Risk analytics
  • 2.4 Digital analytics
• 3 Challenges
• 4 See also
• 5 References
• 6 External links

Analytics vs. analysis[edit]

Analytics is a two-sided coin. On one side, it uses descriptive and predictive models to gain valuable knowledge from data - data analysis. On the other, analytics uses this insight to recommend action or to guide decision making - communication. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology. There is a pronounced tendency to use the term analytics in business settings e.g. text analytics vs. the more generic text mining to emphasize this broader perspective.^{[citation needed]}

Examples[edit]

Marketing optimization[edit]

Marketing has evolved from a creative process into a highly data-driven process. Marketing organizations use analytics to determine the outcomes of campaigns or efforts and to guide decisions for investment and consumer targeting. Demographic studies, customer segmentation, conjoint analysis and other techniques allow marketers to use large amounts of consumer purchase, survey and panel data to understand and communicate marketing strategy.
Web analytics allows marketers to collect session-level information about interactions on a website using an operation called sessionization. Those interactions provide the web analytics information systems with the information to track the referrer, search keywords, IP address, and activities of the visitor. With this information, a marketer can improve the marketing campaigns, site creative content, and information architecture.
Analysis techniques frequently used in marketing include marketing mix modeling, pricing and promotion analyses, sales force optimization, customer analytics e.g.: segmentation. Web analytics and optimization of web sites and online campaigns now frequently work hand in hand with the more traditional marketing analysis techniques. A focus on digital media has slightly changed the vocabulary so that marketing mix modeling is commonly referred to as attribution modeling in the digital or mixed-media context.
These tools and techniques support both strategic marketing decisions (such as how much overall to spend on marketing and how to allocate budgets across a portfolio of brands and the marketing mix) and more tactical campaign support in terms of targeting the best potential customer with the optimal message in the most cost effective medium at the ideal time. An example of the holistic approach required for this strategy is the Astronomy Model.

Portfolio analysis[edit]

A common application of business analytics is portfolio analysis. In this, a bank or lending agency has a collection of accounts of varying value and risk. The accounts may differ by the social status (wealthy, middle-class, poor, etc.) of the holder, the geographical location, its net value, and many other factors. The lender must balance the return on the loan with the risk of default for each loan. The question is then how to evaluate the portfolio as a whole.
The least risk loan may be to the very wealthy, but there are a very limited number of wealthy people. On the other hand there are many poor that can be lent to, but at greater risk. Some balance must be struck that maximizes return and minimizes risk. The analytics solution may combine time series analysis, with many other issues in order to make decisions on when to lend money to these different borrower segments, or decisions on the interest rate charged to members of a portfolio segment to cover any losses among members in that segment.

Risk analytics[edit]

Predictive models in banking industry is widely developed to bring certainty across the risk scores for individual customers. Credit scores are built to predict individual’s delinquency behaviour and also scores are widely used to evaluate the credit worthiness of each applicant and rated while processing loan applications.

Digital analytics[edit]

Digital analytics is a set of business and technical activities that define, create, collect, verify or transform digital data into reporting, research, analyses, recommendations, optimizations, predictions, and automations.^[2]

Challenges[edit]

In the industry of commercial analytics software, an emphasis has emerged on solving the challenges of analyzing massive, complex data sets, often when such data is in a constant state of change. Such data sets are commonly referred to as big data. Whereas once the problems posed by big data were only found in the scientific community, today big data is a problem for many businesses that operate transactional systems online and, as a result, amass large volumes of data quickly.^[3]
The analysis of unstructured data types is another challenge getting attention in the industry. Unstructured data differs from structured data in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation.^[4] Sources of unstructured data, such as email, the contents of word processor documents, PDFs, geospatial data, etc., are rapidly becoming a relevant source of business intelligence for businesses, governments and universities.^[5] For example, in Britain the discovery that one company was illegally selling fraudulent doctor's notes in order to assist people in defrauding employers and insurance companies,^[6] is an opportunity for insurance firms to increase the vigilance of their unstructured data analysis. The McKinsey Global Institute estimates that big data analysis could save the American health care system $300 billion per year and the European public sector €250 billion.^[7]
These challenges are the current inspiration for much of the innovation in modern analytics information systems, giving birth to relatively new machine analysis concepts such as complex event processing, full text search and analysis, and even new ideas in presentation.^[8] One such innovation is the introduction of grid-like architecture in machine analysis, allowing increases in the speed of massively parallel processing by distributing the workload to many computers all with equal access to the complete data set.^[9]
Analytics is increasingly used in education, particularly at the district and government office levels. However, the complexity of student performance measures presents challenges when educators try to understand and use analytics to discern patterns in student performance, predict graduation likelihood, improve chances of student success, etc. For example, in a study involving districts known for strong data use, 48% of teachers had difficulty posing questions prompted by data, 36% did not comprehend given data, and 52% incorrectly interpreted data.^[10] To combat this, some analytics tools for educators adhere to an over-the-counter data format (embedding labels, supplemental documentation, and a help system, and making key package/display and content decisions) to improve educators’ understanding and use of the analytics being displayed.^[11]
One more emerging challenge is dynamic regulatory needs. For example, in the banking industry, Basel III and future capital adequacy needs are likely to make even smaller banks adopt internal risk models. In such cases, cloud computing and open source R (programming language) can help smaller banks to adopt risk analytics and support branch level monitoring by applying predictive analytics.^{[citation needed]}

See also[edit]

Analysis Big data Business analytics Business intelligence Complex event processing Data mining Data presentation architecture Learning analytics

List of software engineering topics Machine learning Online analytical processing Online video analytics Operations research Predictive analytics Prescriptive analytics Statistics Web analytics Smart grid

References[edit]

1. Jump up ^ Kohavi, Rothleder and Simoudis (2002). "Emerging Trends in Business Analytics". Communications of the ACM 45 (8): 45–48. 
2. Jump up ^ Phillips, Judah "Building a Digital Analytics Organization" Financial Times Press, 2013, pp 7–8. 
3. Jump up ^ Naone, Erica. "The New Big Data". Technology Review, MIT. Retrieved August 22, 2011. 
4. Jump up ^ Inmon, Bill; Nesavich, Anthony (2007). Tapping Into Unstructured Data. Prentice-Hall. ISBN 978-0-13-236029-6. 
5. Jump up ^ Wise, Lyndsay. "Data Analysis and Unstructured Data". Dashboard Insight. Retrieved February 14, 2011. 
6. Jump up ^ "Fake doctors' sick notes for Sale for £25, NHS fraud squad warns". London: The Telegraph. Retrieved August 2008. 
7. Jump up ^ "Big Data: The next frontier for innovation, competition and productivity as reported in Building with Big Data". The Economist. May 26, 2011. Archived from the original on 3 June 2011. Retrieved May 26, 2011. 
8. Jump up ^ Ortega, Dan. "Mobililty: Fueling a Brainier Business Intelligence". IT Business Edge. Retrieved June 21, 2011. 
9. Jump up ^ Khambadkone, Krish. "Are You Ready for Big Data?". InfoGain. Retrieved February 10, 2011. 
10. Jump up ^ U.S. Department of Education Office of Planning, Evaluation and Policy Development (2009). Implementing data-informed decision making in schools: Teacher access, supports and use. United States Department of Education (ERIC Document Reproduction Service No. ED504191)
11. Jump up ^ Rankin, J. (2013, March 28). How data Systems & reports can either fight or propagate the data analysis error epidemic, and how educator leaders can help. Presentation conducted from Technology Information Center for Administrative Leadership (TICAL) School Leadership Summit.

External links[edit]

• INFORMS' bi-monthly, digital magazine on the analytics profession

Look up analytics in Wiktionary, the free dictionary.

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Categories:

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• Financial data analysis
• Mathematical finance
• Formal sciences
• Business terms
• Business intelligence
• Big data

“Pervasive” Fraud by our “Most Reputable” Banks

 

Posted on February 28, 2013  New Economic Perspectives

 

By William K. Black


A recent study confirmed that control fraud was endemic among our most elite financial institutions.  Asset Quality Misrepresentation by Financial Intermediaries: Evidence from RMBS Market.  Tomasz Piskorski, Amit Seru & James Witkin (February 2013) (“PSW 2013”).
The key conclusion of the study is that control fraud was “pervasive” (PSW 2013: 31).

“[A]lthough there is substantial heterogeneity across underwriters, a significant degree of misrepresentation exists across all underwriters, which includes the most reputable financial institutions” (PSW 2013: 29).

Finance scholars are not known for their sense of humor, but the irony of calling the world’s largest and most harmful financial control frauds our “most reputable” banks is quite wondrous.  The point the financial scholars make is one Edwin Sutherland emphasized from the beginning when he announced the concept of “white-collar” crime.  It is the officers who control seemingly legitimate, elite business organizations that pose unique fraud risks because we are so loath to see them as frauds.
The PSW 2013 study confirmed one form of control fraud and provided suggestive evidence of two other forms that I will discuss in a future column.  The definitive evidence of control fraud that PSW2013 identifies is by mortgage lenders who made, or purchased, mortgages and then resold them to “private label” (non-Fannie and Freddie) financial firms who were creating mortgage backed securities (MBS).  The deceit they documented by the firms selling the mortgage loans consisted of claiming that the loans did not have second liens.  The lenders knowingly sold mortgages they knew had second liens under the false representations (reps) and warranties that they did not have second liens.  (The authors confirm the point many of us have been making for years – the banks that fraudulently sold fraudulent mortgages did have “skin in the game” because of their reps and warranties.  The key is that the officers who control the banks do not have skin in the game – they can loot the banks they can control and walk away wealthy.)  The PSW 2013 study documents that the officers controlling the home lenders knew the representations they made to the purchasers as to the lack of a second lien were often false (pp. 2, 5 n. 6), that such deceit was common (p. 3), that the deceit harmed the purchasers by causing them to suffer much higher default rates on loans with undisclosed second liens (pp. 20-21), and that each of the financial institutions they studied – the Nation’s “most reputable” – committed substantial amounts of this form of fraud (Figure 4, p. 59).
The most interesting reaction to the PSW 2013 study is that of a fraud denier, The Economist’s “M.C.K.”  In his January 25, 2013 column, (“Just who should we be blaming anyway?”)
M.C.K. argued that we should blame the victims of the fraud (“the real wrongdoers were not those who sold risky products at inflated prices but the dupes who bought them….”).
Only three weeks later, in his February 19, 2013 column discussing the PSW 2013 study, M.C.K. admitted that fraud by banks had played a prominent role in the crisis.

“BUBBLES are conducive to fraud. Buyers become less careful about doing their due diligence when asset prices are soaring and financing for speculation is plentiful. Unscrupulous sellers exploit this incaution. The victims are none the wiser as long as the bubble continues to inflate.”

I will explain in a later column why I believe this passage is badly flawed, but my point here is that the fraud denier and “blame the victim” columnist has recanted.

“During America’s housing bubble, mortgage originators were told to do whatever it took to get loans approved, even if that meant deliberately altering data about borrower income and net worth. Many argue that the banks that bundled those loans into securities deliberately and systematically misled investors and private insurers about the risks involved. It is easy to be unsympathetic in the absence of hard evidence. As I argued in a previous post , ‘investors were not forced to take the losing side of so many trades.’
While I stand by that view, a new paper by Tomasz Piskorski, Amit Seru, and James Witkin convincingly argues that banks deliberately misrepresented the characteristics of mortgages in securities they pitched to investors and bond insurers. The misrepresented loans defaulted at much higher rates than ones that were not—a result that would not be produced by random errors. Moreover, the share of loans that were misrepresented increased as the bubble inflated. The authors estimate that underwriters may be liable for about $60 billion in representation and warranty damages (emphasis in original).”

These two paragraphs are worth savoring in some detail.  The central point we have been arguing for years is now admitted – and treated as a universally known fact: “mortgage originators were told to do whatever it took to get loans approved, even if that meant deliberately altering data about borrower income and net worth.”  The crisis was driven by liar’s loans.  By 2006, half of all the loans called “subprime” were also liar’s loans – the categories are not mutually exclusive (Credit Suisse 2007).  As I have explained on many occasions, we know that it was overwhelmingly lenders and their agents (the loan brokers) who put the lies in liar’s loans.
The incidence of fraud in liar’s loans was 90% (MARI 2006).  Liar’s loans are a superb “natural experiment” because no entity (and that includes Fannie and Freddie) was ever required to make or purchase liar’s loans.  Indeed, the government discouraged liar’s loans (MARI 2006).  By 2006, roughly 40% of all U.S. mortgages originated that year were liar’s loans (45% in the U.K.).  Liar’s loans produce extreme “adverse selection” in home lending, which produces a “negative expected value” (in plain English – making liar’s home loans will produce severe losses).  Only a firm engaged in control fraud would make liar’s loans.  The officers who control such a firm will walk away wealthy even as the lender fails.  This dynamic was what led George Akerlof and Paul Romer to entitle their famous 1993 article – “Looting: the Economic Underworld of Bankruptcy for Profit.”  Akerlof and Romer emphasized that accounting control fraud is a “sure thing” guaranteed to transfer wealth from the firm to the controlling officers.
M.C.K. now admits that liar’s loans were endemically fraudulent and that it was lenders and their agents who “deliberately” put the lies in liar’s loans.   Given the massive number of liar’s loans and the extraordinary growth of liar’s loans (roughly 500% from 200-2006) it is clear that that they were the “marginal loans” that caused the housing markets to hyper-inflate and created the catastrophic losses (in the form of loans, MBS, and CDOs) that drove the financial crisis.  The key fact that must be kept in mind is that once a fraudulent liar’s loan begins with the loan officer or broker inflating the borrower’s income and suborning the appraiser into inflating the home appraisal the subsequent sales of that mortgage (or derivatives “backed” by the mortgage) by private parties will be fraudulent.
The authors of the PSW 2013 study expressly cautioned that their data allowed them to examine only two of the varieties of fraud.  Lenders’ frauds in originating and selling liar’s loans were far more common, and far more harmful, than the two forms of fraud the PSW study was able to study.  The many forms of mortgage frauds by lenders and their agents, of course, were cumulative and the frauds interact to produce greatly increased defaults.
The greatest importance of the PSW 2013 study is that even the fraud deniers have to admit that our most prestigious banks were the world’s largest and most destructive financial control frauds.  Given this confirmation that the banks engaged in one form of control fraud in the sale of fraudulent mortgages (false representations about second liens), there is no reason to believe that their senior officers had moral qualms that prevented them from becoming even wealthier through the endemic frauds of liar’s loans and inflated appraisals.  Appraisal fraud is almost invariably induced by lenders and their agents.  Given the “pervasive” willingness of the officers controlling our most prestigious banks to enrich themselves personally by lying about the presence of second liens, they certainly cannot have any moral restraints that would have prevented them from creating the perverse incentives that caused loan officers and brokers to put the lies in liar’s loans and to induce appraisers to inflate appraisals – two other control fraud schemes that were far more “pervasive” (and even likelier to produce severe losses) than the two forms of fraud studied by the PSW 2013 authors.
Once the fraud deniers have to admit that one form of control fraud involving mortgages was “pervasive” among our most prestigious banks, it becomes untenable to ignore the already compelling evidence that other forms of control fraud involved in the fraudulent origination and sale of mortgages and mortgage derivatives were even more pervasive at hundreds of financial institutions.  The PSW 2013 study destroyed the myth of the Virgin Crisis.  It also exposes the falsity of the ridiculous “definition” of mortgage fraud that the Mortgage Bankers Association (MBA) foisted on the FBI and the Department of Justice that implicitly defines control fraud out of existence for mortgage lenders.  Attorney General Holder and President Obama have no excuse for their faith in the Virgin Crisis, conceived without fraud and should repudiate the MBA definition immediately and train the regulators and agents to spot and prosecute the epidemic of control frauds that drove this crisis (and the S&L debacle and Enron-era frauds).

Business Ethics Listing

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The main article for this category is Business ethics.

[edit] See also

• Category:Welfare economics
• Category:Corruption
• Category:Legal ethics
• Category:Professional ethics

Subcategories

This category has the following 22 subcategories, out of 22 total.

A ► Anti-corporate activism‎ (7 C, 49 P) B ► Blood diamonds‎ (2 C, 33 P) ► Business ethics cases‎ (2 C, 17 P) ► Business ethics organizations‎ (1 C, 16 P) C ► Child labour‎ (5 C, 59 P) ► Commercial crimes‎ (8 C, 45 P) ► Confidence tricks‎ (3 C, 68 P) ► Cooperative principles‎ (2 P)

C cont. ► Corporate conduct‎ (3 C, 2 P) ► Corporate scandals‎ (4 C, 79 P) E ► Environmental economics‎ (13 C, 187 P) ► Ethical banking‎ (17 P) ► Ethical investment‎ (1 C, 13 P) ► Ethically disputed business practices‎ (3 C, 49 P) F ► Fair trade‎ (1 C, 28 P) ► False advertising‎ (1 C, 5 P)

F cont. ► Fraud‎ (22 C, 119 P) L ► Labour relations‎ (14 C, 115 P) M ► Market failure‎ (2 C, 41 P) S ► Social responsibility‎ (31 P) U ► Unfree labor‎ (9 C, 20 P) W ► Whistleblowing‎ (4 C, 9 P)

Pages in category "Business ethics"

The following 121 pages are in this category, out of 121 total. This list may not reflect recent changes (learn more).

Business ethics * Hypernorms A Accounting ethics Accounting scandals Anatomy of Greed Anti-consumerism Anti-sweatshop B Being Globally Responsible Conference Bribery Act 2010 Burson-Marsteller Business and Professional Ethics Journal Business Ethics Quarterly C Center for Adult Development Centre for Research on Multinational Corporations Compliance and ethics program Conflict of interest editing on Wikipedia Conspiracy of Fools Contingent work Corporate behaviour Corporate citizenship Corporate crime Corporate social responsibility Corporate sustainability Corruption of Foreign Public Officials Act Cost the limit of price Creative destruction The Crooked E: The Unshredded Truth About Enron CUC International D Destructionism Diversity (business) Don't be evil Double bottom line Dress code (Western) E Earnings quality Eating your own dog food Electronic retailing self-regulation program Employee monitoring Employee raiding Enron Enron Code of Ethics Equator Principles

E cont. Ethical Company Organisation Accreditation Ethical Consumer Ethical consumerism Ethical implications in contracts Ethics in pharmaceutical sales Ethics Resource Center Evolution of corporate social responsibility in India F Facilitating payment Fair value The FCPA Blog Financial privacy Foreign Corrupt Practices Act Foreign official FRISK Software International G Global Reporting Initiative H Hostile work environment I Inclusive business Industrial espionage Institute for Business and Professional Ethics Institute of Business Ethics Integrity management Interfaith Center on Corporate Responsibility International Anti-Bribery Act of 1998 International Association for Business and Society J Jewish business ethics Journal of Business Ethics Journal of Business Ethics Education L List of corporate scandals M Mandatory labelling Manifesto Global Economic Ethic Marketing ethics MBA Oath McJob Minecode Minority business enterprise Moral hazard N Nishkam Karma O Organizational ethics P Linda Peeno Pension spiking Perfect Relations

P cont. Philosophy of business Power harassment Precarious work Precarity Price discrimination Proceedings of the International Association for Business and Society Product stewardship Public relations R Repugnant market Resource Conservation and Recovery Act The Rich Boys Rules of the garage S Salad Oil Scandal Sexual harassment Sherpa (association) Smart casual Impact investing Society for Business Ethics Society of Corporate Compliance and Ethics Stakeholder (law) Sullivan principles Surface Transportation Assistance Act Sustainability Accounting Standards Board Sweatshop Symantec T Terror-free investing TG Soft Third-party technique Tone at the top Triple bottom line U Unfree labour United Nations Global Compact Universal Accreditation Board University of Illinois clout scandal V Value of life Voluntary compliance W Walmarting Whistleblower Workplace bullying

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Categories:

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• Applied ethics
• Social ethics