DENR partners with ADB in development of interactive dashboard using AI and ML to monitor online illegal wildlife trade in the Philippines

Customer success story: DENR partners with ADB in development of interactive dashboard using AI and ML to monitor online illegal wildlife trade in the Philippines

Posted by Senti AI

January 13, 2022


Illegal Wildlife Trade (IWT) is one of the most lucrative crimes globally. The Philippines, being one of the megadiverse countries in the world, has become a source, a transit point, and even a consumer of trafficked wildlife. 

As environmental organized crime began to proliferate and take advantage of the various online platforms, wildlife enforcement agencies such as the Department of Environment and Natural Resources (DENR) must keep up by developing tools and innovations using technology to track down criminals and prevent IWT. 

To speed up the monitoring process of online IWT, the Asian Development Bank (ADB) and DENR-Biodiversity Management Bureau (BMB) worked with Senti AI to develop an interactive dashboard that shows online IWT data mined by the  University of Helsinki Laboratory of Interdisciplinary Conservation Sciences (HELICS) in Finland. 


The Challenge 

IWT is one of the most lucrative crimes worldwide, and is estimated to have a value of $8 billion to $10 billion every year according to the United Nations Office on Drugs and Crime.

The Philippines, in particular, has been a source, transit , and destination point  of IWT. As a country rich in biodiversity, IWT in the Philippines is approximately valued at ₱50 billion ($1 billion) a year. This estimate includes the market value of wildlife, damage to habitats from poaching and potential ecotourism revenue losses.

The value of IWT is set to increase with the increasing shift to online platforms, which is an easier spot for criminals to conduct transactions without getting caught. A recent TRAFFIC report showed that advertised reptiles on Facebook, for example, have an estimated value of about ₱26.5 million ($570,148).

Currently, enforcement officers and investigators of the DENR exhaust time and energy in manually scouring and investigating  through digital platforms 

It is due to this deficiency that DENR and ADB worked with Senti AI to develop an interactive analytics dashboard that will provide data and make the tracking and monitoring of online illegal wildlife trade data easier. 


The Solution 

Mining data from digital platforms has existed for years. However, recent innovations allowed for filtering platforms that are used for online crime and investigation activities 

Authorities can now easily identify vital insights, patterns, and trends through Artificial Intelligence, Machine Learning and Natural Language Processing. Presenting these data in an interactive analytics dashboard will further allow the DENR to investigate online IWT more effectively and efficiently.  

With the support of the DENR-ADB/Global Environment Facility (GEF) Project on Combating Environmental Organized Crime in the Philippines, HELICS was able to mine and create a database from a list of  158 species of threatened flora and fauna  gathered from various online sites such as Flickr, Google, Google News, Twitter, and Youtube. 

Senti AI then connected the database from HELICS to a data cloud storage built on the Google Cloud Platform and used Google Data Studio to visualize the captured data. Data Studio presents the data in an interactive dashboard that shows information about the most trafficked species and the biggest environmental crime hotspots, just to name a few. 


The Result

Senti AI was able to create a dashboard that visualizes data collected by HELICS. By using data pre-processing approaches to identify relevant data, the dashboard supports DENR-BMB’s effort in identifying digital IWT conversations and made it easier for them to track without having to exhaust too much time and resources in the process.


Senti AI made use of Google Cloud Platform’s Dialogflow to create the conversation flow, which was supported by Senti’s NLU. This was used to answer low-level, repetitive questions, and facilitate the hand-over to human agents if needed.

The fulfillment engine architecture consists of 3 different firestore databases and a cloud run that handles the routing of which state the conversation is in and which response will be given to the user.

Senti AI’s conversation flow was integrated into Genesys’s call center management system.


Aside from lightening the load off of human agents and allowing customers to resolve their issues without queueing up, financial institutions saw reduced costs incurred from manual intervention. A voicebot, for example, helped a Philippine bank with managing high call volumes by conducting over 60% sessions and seamlessly connecting customers to human agents if needed.

One research even suggested that chatbots can help businesses **save over $8 billion a year** by 2022.

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