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Data-driven decision-making for economic prosperity and good governance – II

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By Dr. Ranga Prabodanie

(The first part of this article appeared yesterday (05)

The previous part of this article series explained how the great insurgence of digital data has revolutionized the institutional decision-making process in both business and governance. This latter part will look at the Sri Lankan context: Where we are currently and the way forward to a data-driven decision-making culture. Let’s first have a glance at how decisions are made in data-intensive public services.

Education

Schools, universities and vocational training institutions, throughout the country, collect, record and report data on admissions, enrolment, completion, drop-outs, results, graduations and expenditure for various programmes, courses and subjects. The Examinations Department does have digital records of GCE O/L and A/L results. However, the data is used only for the preparation of annual statistical reports, rather than for identifying and resolving problems. Data-driven decision -making extends well beyond preparation of reports. To make informed decisions on new subject streams, curriculum revision, subject promotion, funding and resource allocation, education data should be explored for trends and associations which raise concerns. To ensure equity in resource allocation, inputs and outputs, produced by education institutions, should be compared using appropriate analytical methods. Analysis of data from industry and other stakeholders is also important to identify the skills in demand and academic disciplines with greater potential for entrepreneurship, employment and scientific innovation.

Health

Public health records which include data on patients hospitalisations, symptoms, diagnosis, treatments, progress and side-effects, encompass valuable insights on emerging diseases, their causes, case rates, recovery rates, death rates, hospital congestion, bed occupation, treatment costs, waiting times, medication efficacies and vulnerable groups which could provide directions for healthcare management and budgeting. There is an urgent need for health records to be digitised and analysed to glean emerging trends, patterns and associations. Though some progress has been made in healthcare analytics, for example in understanding the drivers of Dengue outbreaks, the patterns, trends and socioeconomic implications of most widespread diseases are poorly understood.

Lack of competence in health data analytics was evident in the handling of the COVID-19 pandemic. There were instances where even the number of deaths resulting from COVID-19 were misreported and revised. We have seen various professionals, making claims, in the media, such as “next two weeks are critical”, “the country should be closed for three weeks” and “people are dying at home because they avoid going to hospitals”, but what we never hear is, based on what data, over which time period, analysed using which methods, such insights were derived. Sri Lanka has a well-educated population capable of differentiating facts supported by data from mere human perceptions, and perhaps, that is why they do not listen to such claims.

If you listen to the BBC news, you would often hear them reporting the status of COVID-19 in the UK directly citing the source as “according to ONS (Office for National Statistics) estimates”. Then they may present the information with relevant comparisons as, for example, “COVID-19 was the third leading cause of death in England and Wales in September 2021, accounting for 6.6 percent of registered deaths in England and 8.5 percent of deaths in Wales. The two leading causes of death in both countries were ….” In Sri Lanka, we rarely hear such alarming comparisons,based on real data, but poorly supported individual projections, based on intuition. If data-driven analytical outcomes were shared with the public, people would be compelled to listen. Given the rising healthcare costs and economic depression, it is high time to invest in professional health data analytics to understand the trends and associations, to establish the right priorities, and to inform policies, accordingly.

Agriculture

Agriculture is another sector in which data driven decision-making can make a revolutionary change. Some recent developments in the country, including alleged hoarding of rice, milk and sugar to create artificial shortages and to increase prices, are related to lack of reliable data on agricultural production and imports. To avoid such malpractices, particularly in times of crisis, the government authorities have to keep track of data and continuously update and analyse the data to understand the drivers of market demand and supply. Since the government provides fertiliser subsidies, the agricultural authorities should have data on the acres of food crops to be harvested each season. If there are barriers to obtain reliable data, there is technology to help. A research group in Stanford University has developed a scalable yield mapper which can predict crop yield at the field scale based on satellite data. The system has been tested not only in the US but also in Africa and India. Development or acquisition of such technologies would help authorities to monitor the production and supply of food crops and make informed decisions on subsidies and imports.

The government decision to ban agricultural chemicals came under huge criticism as a poorly informed decision. Given the global appeal for organic food, the ban on agrochemicals can have favourable impacts on our economy and wellbeing. It could have secured a competitive advantage for Sri Lankan food products in the global market. Unfortunately, the decision came as a surprise, without supporting facts derived from real data. The evidence on the associations, if any, between water pollution and agrochemicals, chronic kidney disease and agrochemicals, food prices and agrochemical imports, organic fertiliser and agricultural output, and other relevant and measurable factors, should have been elaborated together with predicted outcomes of the decision, both positive and negative. Decisions that are apparently not supported by facts indicate lack of transparency and accountability, a basic principle of good governance. Lack of data-based reasoning can create chaos irrespective of whether a decision is right or wrong.

Public safety

Continued monitoring of crime data is essential for ensuring public safety. Crime data analysis can reveal spatial and temporal patterns of crime, trends, hot spots, vulnerable groups and delinquents. Such insights can inform resource allocation for crime reduction and prevention. The general public is constantly bombarded, by the media, with fresh crime data, such as “a suspect possessing X grams of ICE was arrested in Y”, which now has no significance to the general public. Instead, if the media reports crime trends as, for example, “X percent of the suspects arrested with illegal drugs in 2020 were adolescents in the Y-Z age group…”, it would immediately trigger the attention of parents, schools and other stakeholders. The former is raw data which the analyst has to work with and the average citizen has little to do with, while the latter is one of the insights derived from data which should inform decision making and policy response and thus matters to everyone.

Conclusion

The previous sections of this article pointed out only a few areas of business, public service and governance which can be enhanced via data-driven decision -making. There are several other sectors, such as investment, energy, transport and conservation where data-driven decision making can make a shift towards sustainable development and better living. As a viable starting point, available public service data can be digitized and made available for analysis by researchers and relevant experts. Countries like the UK, the US and Australia have made health, crime and other data available on the public domain, allowing the researchers to explore the data and inform the government. However, a strong policy framework is needed to support, promote and facilitate data-driven decision-making in all those sectors.

Barriers should be expected, and initially, it would be more difficult to change attitudes than to set-up the basic infrastructure. The biggest barriers could be institutional bureaucracy, political influences, special interest groups and disruptive intentions motivated by the fear of losing power, status, and prerogatives. Strong leadership with a sound understanding of the need for evidence-based decision making is essential. Leaders have to understand that the status reported by officers and various parties with vested interests do not always reflect the reality on the ground and hence decisions made on such advice can lead to disasters. Real data is the only dependable and reliable source of ground reality which should guide policy.

The Sri Lankan government has already taken the initial steps to digitize public service data by establishing the Information and Communication Technology Agency (ICTA), committed to implementing digital-governance in Sri Lanka using ICT to access, exchange, and utilize information efficiently. In collaboration with ICTA, some government institutions have taken progressive steps towards data-driven decision making. The Department of Immigrations and Emigration, the Department of Motor Traffic and the Election Commission of Sri Lanka have already introduced online services which autonomously collect and store data in easily analysable formats. Still we have to develop a policy framework and a culture which supports regular analyses of collected data to generate insights and integrate them into the decision-making process.

Gone were the days when institutional decision making was an exercise of sheer authority; today it’s a complex process of collecting, analysing and generating insights from data. People no longer accept mere predictions without well elaborated facts and evidence, nor do they hesitate to challenge poorly informed decisions made on sheer intuition or authority. The data revolution is on-board, demanding all policies, regulations, restrictions, grants, expenses, and all kinds of decisions to be justified by facts and science. Everyone in business, governance and public service will have to change their attitudes and come to terms with the new decision-making culture driven by data and insights.

(The writer is a Senior Lecturer at Wayamba University, Sri Lanka. However, the views and opinions expressed are those of the writer and do not reflect the policy or position of any institution.)



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The Digital Underground

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Illegal Foreign Exchange, Undiyal, Hawala and Money Laundering, A Four-Part Investigative Series

Forex Platforms, Cryptocurrency, AI and the New Financial Battlefield

THE INVISIBLE FINANCIAL EMPIRE – PART III

The Boyfriend Who Was Never Real

Priya, a 34-year-old professional in Colombo, met “David” on LinkedIn. He claimed to work in fintech in Singapore. For six weeks they exchanged messages daily, about work, about life, about a recent trip he had taken to the Maldives. Eventually, the conversation turned, gently and naturally, to money.

“I’ve been trading on this platform, let me show you,” he said, sharing a screenshot of a sleek trading dashboard showing consistent, impressive returns.

Priya invested a small amount first, $500. Within days, her dashboard showed it had grown to $650. She withdrew $100 successfully, just to test it. It worked. Encouraged, she invested more. Then more. Over two months, she transferred a total of $42,000 into the platform.

When she tried to withdraw her full balance, the platform demanded a “regulatory release fee” of $8,000 before funds could be unlocked. She paid it. Then another fee appeared. Then the platform stopped responding altogether. “David” vanished. The trading dashboard, the customer support chat, the entire brokerage, all of it had never been real.

This is what investigators now call “pig butchering”, and, in 2026, the most disturbing development is not the scam itself, which has existed for years, but what now powers it: artificial intelligence has industrialised the entire operation.

From Manual Fraud to Machine-Generated Deception

For most of the past decade, romance-and-investment scams, like the one that targeted Priya, required enormous manual labour. Scam operations, many of them staffed by trafficked workers held against their will in compounds across Myanmar, Cambodia, and Laos, needed real humans to build relationships with victims over weeks, manage fake trading platforms, and respond convincingly to questions.

That labour-intensive model has now been substantially automated. According to financial-crime researchers tracking this shift through 2026, threat actors are standing up entire AI-generated “brokerage” experiences end-to-end, complete with KYC onboarding, branded customer-service chat, animated portfolio dashboards, and falsified live market data feeds, and operating them at industrial scale against multiple victims simultaneously. Generative-AI relationship managers now front the WhatsApp and Telegram conversations that once required real human scammers. AI-cloned regulator letters are generated on demand to justify the fake “release fees” that drain victims a final time before the platform disappears.

What has changed is not the deception itself, it is the production economics. The cost of running a credible synthetic brokerage against one additional victim has collapsed, meaning a single criminal network can now run hundreds of “Davids” simultaneously, each one indistinguishable from a genuine fintech professional until it is too late. (Figure 01)

Sri Lanka: From Victim Pool to Operating Base

Sri Lanka’s relationship to this global scam economy has shifted in an alarming direction over the past two years. The country is no longer only a source of victims, it has become an operating base for the criminal networks themselves.

In April, 2026, Sri Lankan police raided a five-star hotel property, in Ambakandavila, and arrested 150 individuals, including 133 Chinese nationals, 13 Vietnamese nationals, and one Malaysian national, allegedly running a cyber fraud centre with links to international criminal syndicates, based in Myanmar and Cambodia. Investigators say the operation followed a now-familiar regional pattern: recruiters advertise “online marketing” or “data entry” jobs on social media to lure foreign workers to Sri Lanka, confiscate their passports on arrival, and force them to operate scam campaigns under threat.

The Central Bank of Sri Lanka has formally flagged pig-butchering scams as a “developing threat,” warning that foreign scam networks are increasingly targeting overseas nationals through scam farms operating from Sri Lankan soil. A 2026 United Nations report estimated that at least 300,000 people have been trafficked into scam centres across Southeast Asia.

This is not an abstract international problem. It is unfolding in hotels and rented properties across the country, exploiting the same infrastructure, high-speed internet, affordable accommodation, accessible tourist visas, that Sri Lanka has built to attract legitimate digital businesses and tourists.

Where the Money Actually Goes: The Stablecoin Pipeline

Behind every successful pig-butchering scam sits a laundering pipeline that has been transformed almost as dramatically as the scams themselves, and the transformation has a single dominant feature: stablecoins.

According to the Financial Action Task Force’s March 2026, report, drawing on analysis from blockchain intelligence firms Chainalysis and TRM Labs, stablecoins accounted for 84% of the USD 154 billion in illicit virtual asset transaction volume recorded in 2025, the highest share ever observed, and a dramatic jump from just 15% only a few years earlier. TRM Labs separately found that illicit entities received USD 141 billion in stablecoins, in 2025 alone, the highest level observed in five years. (See Table 01)

The scale of state-level abuse is striking. A Russian sanctions-evasion network built around the ruble-pegged stablecoin A7A5 processed more than USD 72 billion in total volume in 2025.

Fighting Fire with Fire: AI on the Defensive Side

The same artificial intelligence reshaping financial crime is also, out of necessity, reshaping the defence against it. Legacy anti-money laundering systems, built on static, rule-based thresholds, have proven badly outmatched by AI-generated fraud operating at machine speed. Research cited by compliance technology analysts suggests that between 90% and 95% of alerts generated by legacy AML systems are false positives, consuming enormous investigator time while genuinely suspicious activity slips through.

This is not a frictionless transition. AI models are notoriously difficult to explain to regulators and examiners in the way traditional rule-based systems are. The practical compromise emerging across the industry is a hybrid model: AI handles the initial scoring and prioritisation of risk, while documented rule-based logic still governs the final decision that must be defensible to a regulator.

The Regulatory Response: Catching Up to the Digital Frontier

Regulators worldwide have begun moving to close the most dangerous gaps exposed by this digital transformation of financial crime. (See Table 02)

What Comes Next

We have now traced this investigation from the centuries-old mechanics of Hawala and Undiyal, through the three-stage architecture that turns criminal proceeds into apparently legitimate wealth, to the AI-generated frontier of digital financial crime reshaping all of it at machine speed.

In our concluding instalment, Part IV: “Sri Lanka at the Crossroads: Economic Consequences, Organised Crime and the Road Ahead”, we bring this series home. We examine precisely what all of this costs Sri Lanka in hard economic terms: lost remittances, exchange rate pressure, tax revenue forgone, and the 2026 FATF evaluation that will determine whether the country’s institutions can demonstrate, with evidence rather than legislation alone, that they are equal to this challenge. We close with a practical policy roadmap.

(The writer, a senior Chartered Accountant and professional banker, is Professor at SLIIT, Malabe.
Views expressed in this article are personal.)

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‘There are no private universities in Sri Lanka’ – some considerations for higher education reform

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Academics involved in education policy like to say that there is no such thing as a private university in Sri Lanka. The only ‘universities’ in the country are state universities; anything else offering degrees is a private higher education institution (HEI). This position is technically accurate. Yet, in the discourse and imagination of the public, private universities are very real – people teach in them, students register in them, families pay fees, and such degree holders enter job markets in Sri Lanka and outside.

For decades, activists concerned for public higher education have ignored or resisted looking at private HEIs, as if such scrutiny would taint them. Others have worked in both types of institutions, carrying practices from each to the other. The apex body governing state universities, the UGC, has, meanwhile, ignored the concept of conflict of interest and appointed individuals in private higher education in committees and leadership positions. It is unsurprising then that some of the ideologies informing private higher education appear in reform agendas in the state sector.

This is a good time then to consider the varying types of private HEIs around us, and to take a look at some of the issues within them in the hope that higher education reform agendas will include private, as well as state higher education.

What is a ‘private university’?

First, some clarifications. In the public imaginary, a ‘private university’ is typically an institution that provides a foreign or local degree for which the student makes a payment. But this broad classification encompasses a host of diverse institutions and types of degrees which I detail below.

The Non-State Higher Education Division (NSHE) of the Ministry of Education has recognised 295 degrees by 32 institutions. Most of these are private companies and include a handful of established, well-known private HEIs that are ‘university like’. The degrees are local degrees conferred by the institutions accredited by the NSHE Division. While private HEIs conferring local degrees must be accredited by the NSHE Division, there appears to be no legal consequence for not doing so. In addition, there are several permutations of the private degree that miss the net of this Division and the Standing Committee on Accreditation and Quality Assurance (SCAQA) that assists this Division.

For one, degrees conferred by foreign universities offered, via these same private HEIs, are not vetted by the NSHE Division. Secondly, there is a growing plethora of private HEIs which have either no physical presence locally or only a dubious presence. The University Grants Commission has notified the public, through their website, that foreign universities listed in the Commonwealth Universities Yearbook and the World Higher Education Database are recognised, but refrained from giving any other details – which degrees? Offered by what modes? These details are not known. Some of the foreign universities in the lists may be legitimate entities in their own land but the degrees conferred locally, in their name, may not adhere to curriculum or teaching specifications of the NSHE Division or the UGC.

Another troubling phenomenon is the ‘top up degree’, which appears to work on the same principle as that of a pre-paid mobile connection: if I have a Diploma or an HND of a sort, I am eligible to complete a course of study which provides me with a degree, usually from a foreign university. The idea that someone who does not initially qualify for a degree programme should be able to work their way towards one is a progressive notion. This is the concept that open and distance learning (ODL) was based on initially, but which is now sadly exploited. ODL models are expected to provide opportunity for learning for those who may be excluded from traditional learning institutions. In Sri Lanka, however, we have seen ODL become a marketplace offering easy to obtain, for-fee qualifications by institutions with little commitment to superior teaching and learning.

Finally, a perusal of the many types of private HEIs and their varied degrees bring to mind another question – how should the private degrees, provided by state institutions (that are not educational institutions), be regulated? Who should do so?

All of these create a host of problems for the public – for hopeful students and parents and trusting employers. For the higher education sector, recruitment of academic staff, too, has become difficult due to this plethora of ambiguous higher education qualifications, as I discussed in a previous Kuppi article (‘Recruiting academics to state universities’).

Some issues in private HEIs – a bellwether for change in state universities

In this second part of this article, I will discuss some aspects of work in private HEIs – albeit the more established institutions – given that such issues may appear in reform agendas in future.

Across state universities, all permanent staff of a specific category are paid according to the same criteria. The picture is not so clear when it comes to private HEIs since they are different entities legally, typically companies. Private HEIs have salary scales and financial incentives that are different to each other. The more established private HEIs reportedly have attractive renumeration packages, possibly a reason for academics of state universities migrating eagerly to such institutions during sabbatical years and on retirement. This may not of course be the case with other less established, or improperly registered HEIs of which we know little. Academic staff of these more accepted private HEIs seem to value the high financial remuneration they receive (in comparison to state universities) as something that makes their work rewarding.

Attractive remuneration is important to sustain the good life and is at times seen as the institution’s way of encouraging good work. Yet, this has implications for the future of the institution: to continue to deliver on promised financial packages, institutions must continue to have large profit margins. One strategy has been to enroll multiple cohorts of students per year, even up to three or four intakes per year. This can result in exploitative work conditions, since staff must cater to all these cohorts in that same year. If there is inadequate staff, employees are further burdened. On the other hand, if there is a sudden drop in enrolments (degrees can go out of fashion) unexpected layoffs occur. Similar to other sectors that employ short-term contract staff – including state universities – in private HEIs, too, individual teachers, who are on short term contracts that need regular renewal, can feel pressured to work under difficult or exploitative conditions.

At the same time, even in the more established private HEIs, work norms differ from those of state universities in that they include promotional work that keeps the institution’s name in the eye of the public. The Marketing (or similarly named) unit comes up in conversations as one of the most important departments. It appears to weigh in on decision-making related to the number of staff, the amount of re-sits per exams, and other pedagogically important matters. This is a worrying example of how financial rationales interfere with pedagogically or academically sound processes, resulting in problematic results in the classroom. On the plus side, junior colleagues, who had experience in both state and private HEIs, also felt that they faced less harassment in private HEIs – primarily due to the private HEIs ability to take swift action in reported cases of harassment. This is a real indictment on state institutions and their reluctance to address chronic issues of harassment in our universities.

Yet, while we hear much about problems in state universities, we hardly hear of problems that staff in private HEIs face. One rationale for a lack of public expressions by staff is that expressions of discontent might lead to trouble given the importance of reputation for private HEIs. The worry about reputational damage is a growing concern in state universities, too, as evidenced by social media policies and internal conversations on reputational damage, consequent to negative publicity. Institutional worries of reputational damage are harmful in the long run since these impact not only freedom of expression by student and staff, but also research that is possible in and about the education sector.

Some thoughts at the end…


A close look at the private higher education sector is important given its strong presence in the country. Impending reform needs to regulate this diverse array of higher education offerings in the private sector, as well as the state institutions that offer privately-funded options of higher education (a topic for a separate Kuppi on its own). It is time we carefully considered how to build a whole system of higher education out of this broken mess.

Kaushalya Perera is a senior lecturer at the University of Colombo.

Kuppi is a politics and pedagogy happening on the margins of the lecture hall that parodies, subverts, and simultaneously reaffirms social hierarchies.

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Ready for solo spotlight

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Nish Peiris: Excited about future plans

Singer Nish Peiris is set to take the next big step in her music journey.

The talented vocalist, who has been seen and heard in the scene here for a short while, and was also featured with the now-defunct band, Inner Vision, has announced that she will be fully committing to her solo career, after completing her degree this year.

“I’m finishing my degree this year, and after that I’ll be fully committing to my solo music career,” Nish told The Island.

“I’ve already got a few tours lined up for next year, so I’m really excited for what’s ahead.”

Fans, no doubt, will remember Nish for her smooth voice and stage presence, and the good news is that she is now ready to chart her own path and bring new music to audiences at home and abroad.

With tours already planned for 2027, the year 2026 promises to be an exciting year for the young artiste as she steps into the spotlight on her own.

We wish Nish every success in this new chapter!

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