Tag: government

Using text analytics to detect fraud related to government tenders

In my last article, I talked about using statistics to detect fraud.  I’d promised to write about methods for detecting and preventing fraud in the issuing of tenders.

The floating of tenders is the primary mechanism by which governments – often the biggest economic force in a geographical area – procure services from private organizations.

If the bidding process is compromised, contracts might end up not going to the best or most efficient vendor as a stakeholder (the tax-payer) would desire.

That in turn results in bad roads, poor infrastructure, delayed projects, under-spending on education, over-spending on military purchases and other problems associated with bad governance. (See: https://aiaioo.wordpress.com/2012/11/18/tools-for-the-mind-and-how-you-can-change-the-world/).

It also stands to reason that if you could detect tendering fraud, you could solve quite a few of the problems that affect places where corruption is rife.

So how do you tell if a tender has been fairly issued or if it has been gamed to the benefit of a certain participant?

An unfair procurement process can use one of the following methods to ensure that a contract is awarded to a favored party:

Method 1:  Choice of pre-selection criteria

One method used to favour a certain party is the introduction of unnecessary qualifying conditions in the tender that have nothing whatsoever to do with the the end product or service to be procured.

These conditions are added to the tender in order to ensure that only a chosen small set of bidders meet all the conditions for participation in the tender process.

Method 2:  Cancellation and reissuing of the tender

I have been given to believe (by various sources) that in India/China, 3% of the size of the deal is the norm for kickbacks.

If a very efficient bid is placed, and it brings the cost of the service down so that the 3% kickbacks do not translate into a lot of money or if the winner of the bid refuses to pay a bribe, procurement officials might be able to subvert the process by coming up with reasons to cancel or terminate the tender.

They can then reissue the tender with tighter criteria intended to disqualify the uncooperative bidder.

My Experiences

Now, I must tell you that since the beginning of my career as an entrepreneur in India, I have come across numerous stories of terminated tenders, or of the disqualification of firms from a bidding process because they bid too low to be able to pay much by way of bribes.

I have personally walked into a tendering meeting where the government officials began with the words: “Ladies and gentlemen, we are proud to welcome you to our campus today.  We are extremely sorry that we cannot entertain you the way you entertain us when we come to your campuses.”

The tender was being issued for a software project that I felt should have taken a team of 3 engineers no more than 6 months to deliver.  But the tender stated that only firms with a minimum of “100 crores in revenue each year for the past 5 years,” (approximately 20 million USD each year) could bid for the project.  There were only 6 other firms in the room.

When the officials realised that Aiaioo Labs was a small firm, they suggested we leave.

They said, “There should be some other things we can work on with you.  Let’s meet some other time.”

Over the years, I began to wonder if there was any way that I as a tax payer might protect myself from bad deals (corrupt or price-inefficient deals) entered into by government middlemen with my tax money.

Fortunately, it seems possible to use text analytics to detect and alert an ombudsman to possible fraud in the issuing of tenders.  Below is a description of how such a method might work.

Using text analytics to detect irrelevant selection constraints

If tenders for procuring very different products have very similar pre-selection criteria, they could be flagged as suspicious.

The reason this method might work is that relationships between corrupt officials and client firms can take a considerable amount of time to form (because of the risks involved and the consequent need for caution).  It is easy for corrupt officials to change the favored vendor very frequently.

That would mean that they would have to keep the criteria of selection of firms more or less unchanged across widely varying tenders and over long periods of time.  So, you might find that tenders small and large, for hardware or for software, (in other words, tenders for different services), but issued by the same organization might – if the tender process has become unfair – employ more or less the same set of selection criteria irrespective of what is being purchased.

Tools can be developed to detect these similarities and flag them up for review.  Such tools would have to be able to detect the portions of the tender document that are related to the bidder, and the portions of the document that are related to the product or service requested.  It would then have to measure the similarity between the bidder-related sections of the tender documents.  It might also be possible to extract only the qualifying criteria and look for similarities there.  It might also be possible to analyse the bidder selection criteria to see if any criteria might be irrelevant to a project, or incompatible with the requirements of the project.

Using text analytics to detect reissued tenders

If a new tender document’s product or service description sections resemble those in an older tender – and if the issuing organization remains the same, it might be possible that the tender has been reissued.  If it is further found that a very low bid had won the bidding in the previous round of tendering, and that the earlier tender had been cancelled, this could be used as a flag to alert an ombudsman.

Using text analytics to detect vendor-oriented constraints

If many of the conditions for participation in the tender are company-specific (properties of companies such as size or earnings) as opposed to capability-specific (experience in a certain technology space), it might raise a red-flag.

Systems to manage tenders

It might be possible to analyse tenders for fraud if tenders are stored in and managed using a tender management system with fraud detection analytics that both serves as a repository for tender documents, as well as manages the submission of bids and monitors the selection procedure and the life-cycle of projects. This would allow governments to maintain not just a history of tender issuers, but also a history of vendors.  By so doing, governments would be able to determine which vendors are reliable and which are not.

Moreover, it would give people issuing and evaluating tenders more confidence in a low bidder (there is always a danger in projects that someone could bid too low and win the project, but then not be able to execute) and hence help reduce costs. So, a tender fraud detection tool could possibly help governments make better decisions regarding vendors of services and reduce corruption in the process of issuing tenders for the procurement of services and products for government.

Graph Algorithms for Fraud Detection

Text analytics algorithms are difficult and expensive to develop.  Fortunately there are other ways to detect tendering fraud. Patterns of favoritism in tender outcomes can be detected from a bipartite graph of issuing organizations and beneficiaries.  If tenders from a particular issuing organization are found to repeatedly favour a specific vendor from a large field of vendors (more than random probabilities would allow), the organization and vendor could be flagged. Price comparisons across tenders can also be made to determine if any prices have exceeded the price range for similar purchases (this will again require text analytics).

Some Theory

There has been a lot of work on corruption by economists in the last 10 years.  One interesting equation that models corruption is the Klitgaard Corruption Equation. The equation is C = R + D – A where C stands for Corruption, R for Rent (quantum of possible illegal earnings from being in a position of responsibility where corruption is possible) and A stands for Accountability. These concepts are explained very well in the following article http://seekyt.com/define-corruption/ (from where I got the following image as well). But Klitgaard does not model one variable that can impact corruption – and that variable is choice. If you increase the choices available to a purchaser, the opportunities for avoiding corruption increase and the likelihood of corrupt transactions occurring decreases.

For example, if everyone in a certain location must only obtain a service from the government office serving their locality, then a person who does not want to pay a bribe does not have the option of travelling to a different office to obtain the service without paying a bribe.  So, if the officer at the local office is corrupt and demands a bribe for rendering a service, then the person has no choice but to cough up the bribe.  This happens in a lot of government offices where registrations have to be performed.  Increasing the choice of service provider lessens the likelihood of people being trapped into giving bribes.

The same forces are at work in the case of tenders.  The strategy of a corrupt tendering official is to artificially reduce the choices of the selectors to only firms that will pay a bribe. Computer systems that fight tendering corruption work by preventing the artificial restriction of choices.

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Digital Democracy and Cutting out the Middleman in Government

Can information technology in general and text analytics in particular help improve the quality of governance?

We believe they can.  In this article, we discuss one problem/weakness with the present system of governance that makes it very susceptible to corruption.  We then present a solution that relies on analytics to mitigate the problem.

Governance

Governance is a service.  An organization (government) provides people in a geographical area with a service called governance.  The organization that provides the service is for all practical purposes a service company owned by all the people to whom the service is provided.

Services provided by government include collecting money and using it to create infrastructure and services for the common good like roads and schools and city planning and waste disposal.

One weakness in the present approach is as follows.

The goals of the service provider may not always be well-aligned with the goals of the people being served.

When corruption exists, these goals may be very poorly aligned indeed.

Misalignment of Goals

Example 1:  Misalignment of Goals in Road Construction

For example, take the construction of a road.  To the people of the city who use roads, what they want in return for paying out money is better roads.  To the governing body who disburses the money, the goal – where corruption is rife – is high kickbacks.

Does Bangalore really not have enough money to build good roads?  It is very likely that our roads are bad not because we don’t have the money or the means to build roads that last, but because our governing body in charge of road repairs repeatedly doles out road maintenance contracts to people who do the road construction authorities favors in return for the contracts.

Example 2:  Misalignment of Goals in Allocating Budgets for Defence and Education

In an article on why India imports vast quantities of arms, we had described how the Indian government was under-spending on education and over-spending on defense procurement.

That article was based on a World Bank report http://www.imf.org/external/pubs/nft/2002/govern/index.htm that mentioned a study that showed that corrupt governments overspend on defence procurement because of the lack of transparency in such deals.

For example, in 2011 and 2012, India committed close to USD 50 billion to purchases of aircraft and ships alone whereas the expenditure towards education was around 12 billion per annum (woefully inadequate for our country).

Here again, we see a complete misalignment of goals.  People in India need education.  The government, however, when given a choice between putting our money into education or into arms, picks the choice that gives it a higher chance of receiving kickbacks.

Both are examples of something we call man-in-the-middle corruption.

One possible solution is to allow people to allocate portions of their income tax to categories of services that we expect our government to provide us.

Goal Alignment

For example, if I am paying Rs. 20,000 in income tax, I might quite reasonably be allowed to allocate say Rs. 10,000 of it to areas of infrastructure that I feel we need to invest in.  I might allocate of 5000 to education and 5000 to health services.  This would give people some measure of control over the use of our money by the governing body.

Moreover, it would give the governing body a deeper insight into the needs of the people, and also put some pressure on it to allocate all public funds according to a similar ratio.

For this to work, the allocation choices offered to people would have to be meaningful.  Meaningful choices may be determined by public discussion and/or referenda.

Any public discussion on the matter would require the use of debate support tools – text analytics tools that help large numbers of people communicate.

We’ve described one such tool that we call an MCT (Mass Communication Tool) in our lab profile.

In essence, what might be needed are text analytics technologies that can support legislation (proposing legislation, modifying legislation, or conducting a referendum on legislation).

Legislation

Much to the point, at this year’s Coling conference, we came across a paper by a student of the Singapore Management University (Swapna Gottipati), on how one might detect suggestions (thoughtful suggestions) in social media messages.  The paper was titled “Finding Thoughtful Comments from Social Media”.  Unfortunately the paper is not yet available online.

There have been attempts to allow people to propose legislation through online communities that don’t seem to work very well as the following article shows you: http://news.yahoo.com/interactive-white-house-secession-petitions-and-presidential-power-235012490.html

But a more successful attempt at using social media is described in this BBC article Why not let social media run the country?, and I quote: “But Nick Jones, deputy director of digital communications at Downing Street … points to the Red Tape Challenge, which has received more than 28,000 comments since it was launched by the prime minister last year and which has a ‘social media element’.  More than 150 pieces of legislation identified by the public as unnecessary have been so far been scrapped.”

I also really like Clay Shirky’s talk on how the internet will one day transform government.  He talks about how freedom of expression is promoted by social media.  What does freedom of speech do?  Well, it allows more ideas to circulate.  The more ideas there are in circulation, the better things (possibly governance) can become.

He talks about a need for an open-source model for generating agreement on ideas and proposes large scale discussion using something like the GIT version control.

He provides examples of legislation dumped on GITHub and his big takeaway seems to be the idea of collaboration without coordination.

He also talks about the need for openness working in two directions (about participatory legislation and not just legislation being visible to everyone), and about the invention of new methods of argument.  Very interesting.

Feedback

Another use of social media in governance is to collect feedback on government policies and decisions.  In that context I want to mention Project Dreamcatcher, an analytics project with a social media component that was used by the Obama campaign in 2012.  Here is an article on Project Dreamcatcher.  It seems to be an extension of feedback monitoring which has been used for customer service.

Summary

There seem to be new possibilities opening up for the use of technology, possibly text analytics technology, in governance.