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The UK boasts a highly efficient and successful water industry, one that leads the way in research, training and accessing new water sources. Providing essential energy, water, sanitation and drainage to all households, businesses and organisations up and down the UK, the social responsibilities of utility providers are paramount. As part of their social obligations, all providers offer social tariffs to ensure domestic customers have access to a discounted service should they be struggling financially. This significant payment reduction is a lifeline for those who genuinely need it, protecting them from entering debt and ensuring continued provision of such essential services. However, these social tariffs can be abused and many customers are mistakenly or fraudulently receiving reduced charges when they are not entitled to them. A PROBLEM FOR PUBLIC SERVICE PROVIDERS To identify the scale of this problem Synectics Solutions worked with a number of the UK’s water companies in 2019 over a period of three months. The water utility providers it worked with covered rural and inner-city areas of the UK. Each of the companies in the pilot were experiencing problems such as: Customers dishonestly obtaining discounted services Failing to declare residency to avoid paying for their service entirely Declining to update their provider when their circumstances changed Without the intelligence in place to flag these potentially fraudulent claims or errors, these companies were exposed to considerable fraud and error and millions in lost revenue. The method that these organisations were deploying previously to address this problem involved using credit reference agency (CRA) data. The annual cost of these procedures for each company was several million pounds annually, and yet the problem continued to be an issue. "One of the reasons this fraud was not being identified through these measures was because a customer’s claim for a discounted service, or a zero-rated account, cannot be accurately verified through credit reference agency matching alone.” “This process in isolation doesn’t provide the data landscape needed to find the explicit qualifying criteria to confirm a genuine claim.” A POWERFUL PARTNERSHIP IN FRAUD PREVENTION To address this problem Synectics worked with the UK Government’s Cabinet Office and took the opportunity to use the National Fraud Initiative (NFI) intelligence database to help these companies understand and identify the scope of fraud or error that they were suffering. The NFI provides an invaluable database of up-to-date insights and offers 19 different data sets against which to match. This vast solution has already helped over 1200 public sector organisations to identify over £1.7 billion in savings from fraud and error.
Creating an effective CBILS and Bounce Bank Loan Scheme (BBLS) post award fraud check and recovery strategy to reduce the hidden costs associated with loan write-offs. The speed at which the Coronavirus Business Interruption Loans Scheme (CBILS) was deployed to ensure UK business stability, at what was probably the most precarious time for British business since the end of World War II, has been a testament to the agility, commitment and professionalism of the UK banking industry. However, as the COVID-19 pandemic unfolds those businesses who have taken out these government backed loans will have to consider a sensible repayment strategy as we emerge from this crisis and they begin trading again. While the UK Government has agreed to underwrite the majority of the loans, financial institutions who have awarded CBILS and Business Bounce-back Loans (BBLS) will still be expected to make efforts to recover them where appropriate. This recovery process will obviously impact these institutions as they deploy costly resources to administer and engage with customers on developing sensible repayment plans. Additionally there are instances where a proportion of the write-off of some loans provided will directly impact banks directly, financially1. The following analysis from one of Synectics Solutions' data aggregation Subject Matter Experts considers how access to collaborative fraud intelligence, enriched with additional business composition data, can help banks to cost effectively create a CBILS / BBLS loan recovery strategy that will help them to identify fraud - as well as avoid wasting valuable resources trying to recover loans from businesses who are still unable to trade. TIME TO PRIORITISE THE RECOVERY FOR CBILS AND BBLS Describing the current situation in the UK as an “unprecedented time” has become cliché over the last 12 weeks. The British economy has been forecast to be potentially facing the biggest recession in over 300 years2, with small to medium sized businesses across the country facing over £105 Billion of unsustainable debt3. Unsurprisingly UK industry has turned to the British Government for support, who in turn have relied upon British banks to spearhead the distribution of the economic stimulus packages - which were speedily put in place to mitigate the risk of total economic collapse in the face of the COVID 19 pandemic. What began with a £30bn stimulus package4 soon turned into £330bn5, with measures mainly focusing on paying staff wages, payment holidays, and propping up businesses that are unable to trade as usual due to the lockdown. However, fears are already growing that UK banks will be saddled with over £36 Billion in toxic debts from loans that will ultimately become unrecoverable as outlined in this recent analysis by The Guardian6. Outside of covering staff costs, stimulus packages for businesses have fallen under two schemes – the Coronavirus Business Interruption Loans Scheme (CBILS) & the Bounce Back Loans Scheme (BBLS). RISK FACTORS Fraud checks were one of the few measures included within the BBLS scheme as it was rolled out. In fact Synectics’ collaborative fraud intelligence database (National SIRA) has been at the heart of the measures in place to help with that and was a resource specifically mentioned within the UK Government’s legislation accompanying the BBLS and CBILS schemes. As a result, Synectics have already processed a large proportion of these loans and are able to easily identify incidences of fraud, where a business may have successfully applied for multiple loans with multiple lenders. For the BBLS, banks have been mandated to underwrite loans without assessing affordability. Now as the dust is beginning to settle, it is essential to begin prioritising post-award fraud checks, collections & recovery processes to effectively identify which businesses should be getting in touch with their banks to arrange a repayment strategy as they begin trading again over the next 12-18 months. However, to create a more effective collection strategy for these loans, and one that goes beyond just a simple fraud check, we would advise analysing a wider set of risk factors to create a much more intelligent recovery strategy that will help to better prioritise cases, that require investigation or recovery - and avoids a costly and ineffective ‘one size fits all’ approach to your collections processes. For the BBLS, banks have been mandated to underwrite loans without assessing affordability. Now as the dust is beginning to settle, it is essential to begin prioritising post-award fraud checks, collections and recovery processes.”
The introduction of predictive analysis techniques to help improve the way that financial services companies can improve their ability to identify risks, such as fraud - or improve the speed at which they can safely on-board new customers - has transformed those businesses who have been able to embrace this technology. On this webinar, Synectics Solutions explores some of the key learnings they have made over the last 2 years while successfully implementing predictive analysis techniques into live business systems for many of the UK’s leading banks and insurers. Some of the issues discussed provide useful insight into; the pitfalls of project managing this complex technology into large organisations BAU systems the business impact of such an implementation some of the wider potential for predictive analysis across the organisations using it.
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