Fixing welfare leaks

Welfare money is rarely discussed in formal terms. People do not call it social protection or fiscal policy; they call it flour, school fees, petrol for the bike, tablets for a father whose blood pressure will not wait. In 2025 the government is putting close to 2.8 trillion rupees into welfare and relief. The Benazir Income Support Programme alone now reaches more than 9.3 million households. That kind of scale is necessary, but it also leaves little room for error.

When mistakes happen, they grow fast. Audits suggesting around 141 billion rupees were lost to fraud and errors are not just figures on a page. That money has a face. It is the grocery list that got shorter. The bill that stayed unpaid.

This is where artificial intelligence has started to slip into the conversation. Not loudly, and not as a magic fix. More like a practical tool doing work people simply cannot keep up with. Welfare systems today sit on piles of data. Identity cards, phone numbers, bank transfers, household records. Caseworkers deal with hundreds of files while deadlines close in and phones keep ringing. Under that pressure, things get missed.

AI does not solve the problem, but it notices patterns quickly. One phone number linked to several families. The same address appearing again and again. Payments moving through the same agents month after month. None of this proves fraud on its own, but it raises flags early.

During recent flood relief and heat emergency payments, these checks mattered. AI-based screening stopped thousands of questionable applications before money went out. Officials involved have admitted that in earlier disasters many of those payments would have gone through. There simply was no time to look closely. Speed mattered more than accuracy. This time, filtering happened quietly in the background. Genuine families were paid faster because obvious abuse had already been filtered out.

There is also the issue of trust, which is harder to measure but just as important. When people hear that welfare money is being stolen, resentment builds. Taxpayers grow impatient. Beneficiaries feel watched. Honest families get stuck in repeated verification cycles because someone else cheated. When bad actors are removed quietly and early, the system feels less hostile.

Our economy is informal and messy by nature. Families share homes, phones, even bank accounts. A rigid system can mistake survival for fraud. This has already happened in smaller trials, and the damage cuts deep. Being wrongly flagged can mean weeks without money that a family depends on. That is why responsive AI measures should put in place. Human review has to stay at the center.

If governance wants to use AI without hurting the people it is meant to help, a few things matter. Data across departments needs constant cleaning. Appeals must be quick and simple. AI should be used to catch fraud and speed up payments, not to monitor lives. Independent audits should be shared openly so people know what is happening behind the scenes.

AI will not end fraud. Nothing will. But even cutting losses slightly could send billions of rupees back into real homes. In a year of high inflation and tight budgets, that matters.

There is also a quieter benefit. Caseworkers are exhausted. Many are drowning in files with little time to actually help anyone. When AI handles obvious red flags, staff can focus on fixing records, explaining eligibility and solving problems. That human contact matters more than any system.

Stability is thin and if technology can help keep a few more families from slipping over the edge, even imperfectly, it is worth using carefully and honestly.