Harnessing the power of Analytics in CSR
A safer neighbourhood for Malaysia through analytics
KUALA LUMPUR, 21 November 2019 — In 2018, over 25,000 motorcycles were reported stolen in Malaysia. Discounting the cases that went unreported, this means an average of 70 motorcycles are stolen each day, nationwide, and countless more inconvenienced by such thefts.
How is this affecting our communities?
Motorcycle theft tops the petty crime list with 25,470 reported cases – a 170% increase from the next most prominent crime - burglary, which stands at 14,699 incidents. This has resulted in an estimated loss of RM50,000,000 from motorcycle theft.
These statistics are particularly alarming as motorcycle theft can be extremely costly and disruptive, especially to the B40 demographic who utilise motorcycles as their main mode of transport. To the average B40 household, the cost of losing a motorcycle can be equivalent to an entire month’s income or more.
This is without considering other forms of inconvenience arising from these incidents, such as the additional effort and costs required to make alternative transportation arrangements and the time taken to acquire replacement motorcycles.
Harnessing the power of Analytics in CSR
In conjunction with Impact Day, a CSR initiative where Deloitte employees nationwide volunteer their time and skills to help out local communities, the Risk Analytics team launched the Neighbourhood Safety Analytics Project, with the intention of achieving a greater impact through actionable insights, creating a safer environment for all Malaysians.
In welcoming this showcase of how data analytics could enhance the quality of life, Surina Shukri, CEO of Malaysia Digital Economy Corporation (MDEC) shared, “MDEC's mission is to build a vibrant digital future for our nation. As such, we welcome this initiative as part of a deeper collaboration with the private sector across all areas. Digital CSR is aligned to Vision 2030, which includes building a more technology savvy nation through the innovative use of new digital services."
How can Analytics help to reduce motorcycle thefts?
Data on petty crime in Malaysia between 2017 and 2018 was gathered from iSelamat, before being analysed with Microsoft Power BI, a Business Intelligence tool. The data analysed was then used to generate actionable insights through a slice-and-dice and visualisation of the locations, dates and patterns in the crime data.
If you know when and where a crime will happen, what action will you take?
With the relevant statistics, preventive measures can then be implemented to target identified crime hotspots and seasons:
- Organise neighbourhood watch patrols at crime hotspots, with increased frequency during ‘peak season’.
- Install surveillance cameras to monitor any suspicious activities.
- Increase number of functioning street lights around the area for better visibility and safety.
- Set up prominent signage and life-sized standees of security officers to generate awareness and serve as a warning to potential thieves.
- Implement the “Broken Window” theory, which involves repairing or removing damaged objects and infrastructure, such as broken signage or abandoned motorcycles. This will improve the general atmosphere and safety of the area, and give the impression that it is well maintained and cared for by local authorities and residents.
The prevalence of motorcycle theft is undoubtedly responsible for significant disruptions to the work and personal matters of our communities, and is an epidemic that must be addressed.
The views and opinions expressed in this article are those of Justin Ong, Financial & Regulatory Risk Leader, Deloitte Malaysia.
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About Deloitte Southeast Asia
Deloitte Southeast Asia Ltd – a member firm of Deloitte Touche Tohmatsu Limited comprising Deloitte practices operating in Brunei, Cambodia, Guam, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Singapore, Thailand and Vietnam – was established to deliver measurable value to the particular demands of increasingly intra-regional and fast growing companies and enterprises.
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