PaymentIntelligence – This post is the extract from my presentation done at GECommunity 2017 Summit in Kuala Lumpur Malaysia.
PaymentIntelligence a new dimension from the combination of Payments with Artificial Intelligence.
– Welcome to the future of Payments
About the Summit
The summit took place on on 12th and 13th December 2017. This was the biggest summit in Malaysia and opening speech was done by “Sri Haji Mohammad Najib bin Tun Haji Abdul Razak”. Sri Haji is the current Prime Minister of Malaysia since 2009. The event was attended by almost 15,000 participant with speakers from 27 countries around the globe.
If any one is looking for complete and detailed presentation free copy, please leave your email address in below comment box. I will talk about different elements of PaymentIntelligence (PI) in this post on very high level only. Idea is to show what is PI, what it can do and what all elements are in ecosystem of PaymentIntelligence.
- Bringing Artificial intelligence to make FinTech better, demystified and simple.
- How PaymentIntelligence will become better with machine learning.
Artificial Intelligence is changing the face of payments so its frauds
To bring people from technical and business backgrounds who can understand, appreciate the need and concept of Artificial intelligence to create products, services and solutions in financial Technology and banking domain. Developments in the FinTech space – an industry which received $19 billion in venture capital last year. Radically transforming the face of financial services, from payment systems and clearing to financial settlements.
PI at national level can bring the whole nation together. It can secure the economies and flush out the underground economies. In a scenario when you are in a shopping mall and get a sms. That reads “Walk on to 3rd floor to buy your favourite brand of cloths to get 20% discount”.
Because its 25th of the month and you got paid meaning have money. So here merchant, bank, employer, telephony service provider, credit service provider and many other are all connected to PI System so this kind of system can generate business. In US alone real-time payment eventually got its the first new core payments infrastructure which the most advanced in more than 40 years.
Now a second scenario on top of first one “you fall short in your balance system to make payment”. In this system case PI system can offer micro credit of $10 to $500 credit depending on your credit ratting. Even if system charge you 10-20% rate of interest for 3 months period (80% over 12 months) and you wont even mind or make note.
PaymentIntelligence (PI) System – Can open lot of opportunities to young entrepreneurs and boost economy of the nation. Data alone is life-less and inherently dumb. It doesn’t do anything alone without support, knowledge and tools on how to use it, how to act on it. Algorithms is where the real value lies. Algorithms define & drive actions. Whole point of the PI algorithms involved in high-frequency in business is to detect, analyse and make decisions faster than a human heartbeat.
FinTech & AI
FinTech Intelligence can help in one way or another how to make money. The application of any computer-enabled algorithm that can be applied against a data set to find a pattern in the data. Because of this, new wild and flashy AI systems that are making FinTech ‘s smart systems smarter and can help to them to fly. Not surprisingly, these companies each have a clear market applications and reduce friction in the business problems they address. Fintech’s Artificial Intelligence revolution is perfect example and era of pervasive AI financial technology services.
AI Boosting info security through behavioural biometric intelligence (BBI)
- With account takeover and mobile fraud growing in leaps and bounds. Need has arise to move beyond password protection and two-factor authentication which any ways has never been greater. Hackers routinely find ways to steal personally identifiable information (PII)
- With BBI Consumers will be able to perform complex transactions. Such as making purchases and payments or transferring funds, without the need to type a single character.
- Behavioral biometrics presents a fairly new approach to monitoring and scoring customer and device interactions during transactions.
- Voice biometrics is been attempted to combine with AI in a bid to directly communicate with chatbots up to the emotional level of understanding. This could probably reduce the need for human operators in call centers in the future while protecting employee perpetrated data leaks
- Companies in the FinTech industry will find multiple opportunities. Such as how to enhance decisions on lending and how to optimise financial advising or execute better trading decisions. AI could also help improve the algorithms to create the optimal financial advising.
As digital business remains dynamic, and business teams become more engaged in the solution delivery process. We expect this will result in a more-complex collection of technologies, disciplines and practices. This collections is to support the varying needs of users. Organizations must plan for the adoption, or replacement, of key technologies and practices, as well as the development of people and skill sets.
Banking Applications with Artificial Intelligence
Application Artificial intelligence has several applications in the banking industry. Top-5 applications of artificial intelligence in the banking industry that will revolutionise the industry in the next 5 years are listed below.
- Algorithmic trading
- Chat bots
- Customer recommendations
- Fraud detection
- AML Pattern Detection.
Blockchain is here already and while still relatively new to the financial space. Its attracting interest from around 90% of banking sector executives though. According to a new study on the potential of the technology in the industry. Blockchain is said to be the answer for many information security threats. For business strategies, customer acquisition plans and revenue goals may be in danger due to (Not limited to though)
- Application tampering
- Rogue Apps
- Overlay attacks
Intelligent tools for Threat Intelligence / Threat Hunting.
What is needed here an intelligent systems to handle Threat Intelligence. The system which will be on 24X7 job for Threat Hunting. Blockchain to push PI (Payment intelligence) at rapid speed as on date 40% of banks find themselves still at the exploration phase of blockchain technologies. While around 30% are pursuing proof of concepts.
Intra-bank cross-border transactions are regarded as the most likely payment system to see blockchain implementation, followed by cross-border remittance and corporate payments. PaymentIntelligence brought real time payment which eventually brought real time threats as well. P2P and m-commerce payments are fueling growth in real time payment systems.
PaymentIntelligence as a Service
One of the technologies which has received considerable hype in recent years is blockchain. A distributed ledger technology (DLT) that serves as the backbone of cryptocurrencies. Application development technologies and disciplines continue to evolve as the need to deliver business outcomes and accelerate application delivery with high quality intensifies. Blockchain’s financial part under payment intelligence encompasses all types of data science algorithms, supervised, unsupervised, segmentation, classification, and regression including deep learning.
That’s simplistic for a reader to appreciate the importance of Regression. Historical notes on Knowledge discovery and data, CRISP-DM, BIG DATA, and Data science and their relationship to data mining and Machine Learning are available all over internet for free. How to put them in real business and discover the hidden potential of such powerful tools to bring values are still not much talked or explored.
Please note I am not advocating AI on blockchain here as basic architecture for both as per my understanding is lot different as on date. Also Deep Learning is different from traditional predictive analytics in my opinion hence my picture above shows the same.
Smart machines producing smart payments with inbuilt payment intelligence. What fascinated me most in PI subject book (Currently being written) the explanation of such complex subject, regression and use of AI and its components. They are described as ‘ a tutor teaching students in an institute – if outcome is continuous use linear and if it is binary, (Regression) use logistics.
New Job role Payment Intelligence and SECaaS Analysts
Soon in market or it must have started already in the market in payments and info-security industry to fight cyber-crimes. These professionals will be great value add to
- Serve as a Global Risk Subject Matter expert on various payment frauds via new brand new platform i.e payment intelligence.
- The new system or PI says will be able to analysis data and response to alerts relative to with SECaaS as built in metric.
- Statistics & machine learning techniques surrounding cybercrime case will reported and investigated in automatic manner.
These professionals will lead the design and production deployment of new and advanced techniques to recognize and prevent payment methods and cybercrime.
Books + Other readings Referred
- Open Internet – NewsPortals, Economic development report papers
- Personal & professional working experience of @AILabPage members.
All credit and credits of contributions remain with original authors and I sincerely thanks for their contribution here. Welcome to the future of Payments
Feedback & Further Question
Do you have any questions about Deep Learning or Machine Learning? Leave a comment or ask your question via email . Will try my best to answer it.
Conclusion – Artificial intelligence is set to transform the financial services industry. How AI will be transforming the future of finTech to elaborate items from the above list in African markets and opportunities are even more dramatic in just the past five years. Africa has payment instruments such as mobile wallets for merchant payments, bill payments, prepaid airtime top-up etc. This infra is ultra modern mobile payment infrastructure with too much packed init.
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