Inspiration
The word metaverse describes a fully-realized digital world that exists beyond the one in which we live. Metaverse is a digital avatar-based universe. It is a virtual reality world where users can interact, play games, and experience things or activities as they would in the real world.
Experts said crypto is the big thing in the meta world. Metaverse is making headlines in technology, there’s a boom in metaverse crypto projects, each viewing to shape the future of virtual world. It is a collision between the digital and physical worlds when virtual reality and augmented reality bridge the gap and allow the physical and virtual worlds to interact closely.
Despite origins in gaming and social media, the Metaverse’s application has extended beyond entertainment to traditional sectors. Several practical applications of Metaverse technology including Virtual Reality (VR) and Augmented Reality (AR) have facilitated the buying and selling of goods in an entirely virtual environment. These virtual environments enable companies to obtain greater exposure without geographical constraints, changing the way in which we shop and make payments altogether. This has led to a redesign of customers’ purchasing decisions and interactions, creating the next evolution of consumer spending patterns.
Metaverse for Financial Services
Aside from above retail influenced usecases, the future of work is also being reshaped by Metaverse technology, with examples being witnessed in the financial services industry. Customer experience, Customer service, remote collaboration, data visualization and many more to complementing existing mobile banking apparatus, such as apps that showcase customers’ account balances or direct them to the nearest bank branches using AR. Branch in metaverse could be in itself big opportunity based on how it evolves as an as an acquisition and customer service channel in competitive world. Beyond being a platform through which above use-cases can be hosted and reproduced, the Metaverse is quickly leading to the creation of a new economy, involving trade in intangible products such as non-fungible tokens (NFTs) in the realm of both physical and virtual assets. Virtual real estate sales are skyrocketing. The monetization of the Metaverse is growing rapidly. This leads to blockchain ecosystem as underlying platform to support these financial transactions in decentralized fashion. This is one of many trigger to raise the importance on the role of regulators. Across domains, transactions require some type of financial infrastructure. Big question that will change the dynamics - Is DeFi will rule the metaverse?
For Financial Services Role of Blockchain and cryptocurrencies have brought together plethora of opportunities for existing enterprises to adopt early and for startups to be unicorns. These opportunities are in form of deFi apps that draws its parallel from tradFi world. Example – staking, treasury management, options, lending.
What it does
AML, KYC and Fraud challenges in deFi apps context
Horizontal narratives in financial services use cases - Anti Money Laundering (AML), Know your customer (KYC) and fraud narratives have been in industry for decades now to combat money laundering and the financing of terrorism. One of the many shifts includes the adoption of automated processes to, as appropriate, permit the filing of suspicious activity reports. This is especially important considering criminals are constantly evolving and looking for new ways to hide and move dirty money. Moreover, wrongdoers are not stalled by the bureaucratic burdens needed to implement resources or having to investigate false positives, therefore they are able to work quicker. It is here where big data, Artificial Intelligence (AI), and machine learning could certainly prove to be highly beneficial. Important point to note is that even after applying AML practices and resources – current systems in tradFi are not able to detect and report most financial crimes, only a tiny fraction of this is recovered through AML systems.
However, this does not mean financial institutions are not putting in the effort to correct this. Investments in AML practices and procedures have been growing year over year and key financial players working together to better streamline the process of combating global financial crime. The global pandemic and shifts in the financial services industry have helped cryptocurrencies and digital payments become increasingly mainstream these past two years. These depicted horizontal narratives AML, KYC and Fraud has gained a lot of traction when it comes to use of cryptocurrencies and their supposed anonymity. It has been wrongly believed that crypto allows for complete anonymity in its usage due to digital currencies not being linked to a name, but to a wallet address. The reality is that blockchain technology is traceable as records stored there are available to the public and almost impossible to alter. While you might not get a first and last name, a scramble of digits that are unique to a digital wallet and of which the activity can be traced are available.
Trends like Cross chain interoperability, guidelines from regulators and compliance teams have initiated the purpose of developing this solution on how enterprise knowledge graph (EKG) could address and be ready for the modeled challenges.
Several legitimate exchanges have been regulated to share information on transactions to have profits from cryptocurrency transactions reported to designated authorities. Group of companies in crypto space set up a platform to help meet conditions of regulations like customer due diligence be performed and the preparation of Suspicious Activity Reports (SARs) and Cash Threshold Reports (CTRs) where appropriate.
Undoubtedly challenges continue to pose in countries where transactions are unregulated. Solution is globally consistent regulatory framework around usage of cryptocurrency and deFi apps. The future of banking no doubt includes cryptocurrencies and as their acceptance continues to grow around the world so do the AML risks related to them and in order to mitigate those risks, AML practices will have to evolve. Banking technology is only in its infancy and only time will tell what the future holds.
How we built it
Legacy approaches to detecting money laundering are insufficient. Virtually all existing anti-money laundering analytics compliance systems are built upon relational databases, which store information (customer, account, transaction, etc.) in rows and columns. Relational databases are great tools for indexing and searching for data, as well as for supporting transactions and performing basic statistical analysis. These are, however, poorly suited to connecting dots and identifying hidden relationships, which is essential for analyzing money trails and assessing their money laundering risk. Such queries could take hours or even days to run, rendering any meaningful analysis of linkages among parties and transactions practically impossible.
Though focus here is on financial services, fraud, AML had impacted multiple industries.
*Graph networks as answer to address cited AML, KYC and fraud challenges integrated with social elements be it feeds from phone usage, digital channels, IoT devices proliferated with 5g adoption, both real and virtual community and avatar persona driven sentiments *
Challenges we ran into
Accomplishments that we're proud of
Early bet is on leveraging the graph convolutional networks along with deep machine learning models specifically GPT-3 as representational approach and associated constructs on how graph tech stack could be front runner to address the cited risks emerging from ecosystem. Graph Analytics Can Reduce False Negatives in Money Laundering Detection. Applying machine learning models on Graph tech stack improves the accuracy of Money Laundering Detection in near real time.
Individual phone usage in terms of number of connected devices, connected cards, connected IP addresses, connected to financial entities like banks, cards, loans, demat and more brings one dataset that connected with social and virtual attributes makes scalable and resilient way towards solution. Social media attributes provide intelligence to drive credit risk evaluation and in discovering patterns of fraud, AML and inputs to customer KYC. Out of many, sentiment analysis generated through hashtags, likes and comments become most important input from digital channels towards implementation through enterprise knowledge graph approach like TigerGraph with analytics and intelligence at core.
What we learned
Graph technology explores beyond traditional paths and patterns to uncover the fraud through connections discovery that gets to next level to determine groups and minimizes false positives. Difference is how graph leverages at least 2-4 levels of relationship called as hops throughout the network of data.
What's next for Tiger.social.aml
How we define a standard framework that covers social platforms and multiple vendors to consumer data of customers ?
Built With
- analytics
- api
- machine-learning
- node.js
- tigergraph
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