Author: vdeorukhkar

The exponential growth in the number of mobile users has also made online banking the preferred choice for bank customers. But with that, also arises the increasing risk of fraudulent attacks and money laundering instances with numbers suggesting that as many as one in four customers fell prey to financial fraud in 2016.

To counter this, security giant Kaspersky recently launched their Fraud Prevention Cloud which incorporates advanced technologies such as Biometrics and Behavioral Analysis models built into the system and an ability to handle big data in the cloud. The information collected by the application integrates with the Enterprise Fraud Management System and enables fraud detection in real time even before the transaction is completed. The approach is based on Humachine Intelligence  – a combination of Big Data and threat research analysis embedded with machine learning algorithms and lots of security expertise.

It uses the concept of Risk Based Authentication (ABA) and assesses the risk before a user gets logged onto a digital channel and provides decision to back end system to allow further access or not. Overall, the Kaspersky Fraud Prevention Cloud acts not only during the login process, but also during the whole session thereby helping the company to minimize risks and avoid losses during an attack. Could this be the most sophisticated system for Fraud Management? We’ll see !

Link – http://www.deccanchronicle.com/technology/in-other-news/150317/kaspersky-anti-fraud-cloud-enables-machine-learning-for-multi-channel-protection.html

With all the forward thinking already concerning FinTech, it is essential to consider its trend going forward.

• Increasing partnership, decreasing competition.

The industry has started to see FinTechs as collaborators. Not only private companies like Paypal and Visa; even banks like Citi are showing a willingness to work with FinTechs rather than against them especially when open banking regulation will take power completely.

• Block-chain will continue to exist.

The banks have and will continue to show interest in Block-chain because of its potential to provide an underlying, interconnected financial services infrastructure.

• Realizing FinTech’s potential to cause a disruption.

Financial services industry that includes Retail banking, Lending, Payments, Wealth Management, Insurance, Markets and Exchange etc. are on their way to be revolutionized with advanced software and hardware advancements made in the technology environment which opens the back door for cross domain, cross platform businesses to operate in more than one industry.

My piece of advice to any job seeker would be to also consider targeting the above mentioned domains within financial services industry as who knows how far this technology could get one to? For those still in doubt. The image below will give you a fair idea of it’s increasing magnitude.

Link – http://www.businessinsider.com/heres-everything-we-learned-about-the-future-of-fintech-at-mobile-world-congress-2017-3

If a poll was to be taken among people who are into making stock investments on a regular basis; I feel a portion of them would admit to consulting brokers and other investment agencies that assist them in making an investment decision. In return, these middlemen get commission from the returns the investor makes. What if, we were to have an application that would obliterate the need of these middlemen and provide a transparent, easy to use application that would do the same (or even a better) job helping people make an investment decision?

Today, technology has advanced to a level that cars can drive themselves to their owners. All these are real world applications of self learning algorithms and predictive data analysis to facilitate decision making. But in order to support such algorithms at scale, it is important to have an equally good hardware underneath. Today, in memory databases are fast changing the way the industry operates and an ability to process data real time is literally an icing on the cake.

Thus, the idea is to imagine an application that is deployed on an in-memory database and supported by self learning algorithms that expand its data processing capability from historical data to online news articles to real time twitter feed and provide the user with recommendations as to whether the chosen company is a good investment decision or not. What do you guys think? Is there a possibility to have such an application at our disposal in the future?

– Thought and compiled by Vaibhav Deorukhkar

My previous post described the underlying financial technology and its current list of consumers. But of what use is such a discussion if there is no one to supplement the technology with a good business idea? What if there is an application that can fetch information regarding an applicant (which probably by no means is an easy task) in a relatively short period of time and come up with reports. Yes, this would involve a high stakes contract with the government and credit unions and what not. We have access to both ends of the technology spectrum – from the latest in memory databases (hardware) to the always enhancing machine learning knowledge (software).

An application (if developed) might involve gathering and storing data from various sources such as – Credit Bureau, DHS etc. And all this needs to be transmitted/ stored in a secure way that would adhere to the data privacy standards. Because, the systems in context are capable of handling such huge data and have a capability to process it as well; I feel that working on plausibility of such an application is not totally out of the blue and might be realized at some point in the future. What are your thoughts?

June 2016 certainly marked Britain off the EU but with that came unprecedented possibility of a negative effect on Britain’s sector. Whether that happens or not is a topic for later discussion but Sweden is certainly making efforts to become a hotspot in the European Fin-tech sector.

Factors in Sweden’s favor:

• An established track record for attracting investments: Highest number of fin-tech investments per capita in the last 5 years within Europe.
• A relative ease of access to the government authorities that aids businesses create a well balanced and favorable ecosystem.
• Sweden is on its way to become the first country to become a cashless society by 2030.
• Formation of the Swedish Financial Technology Association (SweFinTech) that would ease the overall regulatory environment for FinTechs (including upcoming startups as well).

But according to me, if the speculation is true, it is the timing that will matter for Sweden to actually beat UK to top spot. Brexit has ironically opened the floodgates in the form of a wild card entry for many such entities to pierce the European market.

Reference – http://www.newsbtc.com/2017/02/19/sweden-post-brexit-fintech-destination/; https://www.cryptocoinsnews.com/sweden-see-opening-brexit-boost-fintech/

 

A discussed in the previous post, the evolution of machine learning in FinTech, it is time to pin point prospective users to appropriate resources. As discussed before, the application of Machine Learning expands to –

1. Predictive analysis for credit score and bad loans.
2. Support accurate decision making.
3. Information Extraction.
4. Fraud Detection & Identity Management.
5. Building Trading Algorithm
   and many more…

However, for organizations planning to work in the above mentioned areas of expertise (and having no knowledge of how things are done), I feel it is a good idea to seek help (at least in terms of consulting) from businesses who are into this domain.

• Lending Club, Kabbage, LendUp specialize in Predictive analysis for credit scores and bad loans so according to me it is a great go to market option for lending organizations to seek their advice.
• Affirm, ZestFinance, Billguard are into users of data that support decision making. They all consume and analyze vast amounts of data.
• DataMinr, AlphaSense work to achieve real time information discovery and information de-fragmentation respectively.
• Feedzai, Bionym, BioCatch are in different industries but have common interests towards fraud management and they leverage it my natural language processing and machine learning.
• KFC Capital, Binatix are pure finance based firms and use state-of-the-art machine learning algorithms to support their trading algorithms. 

   

  Detailed List at – https://letstalkpayments.com/applications-of-machine-learning-in-fintech/

The FinTech startup ecosystem infused about $14.5 Bn globally just in venture funding in 2015 which is almost twice from what it gathered in 2014 and next year, an astounding amount of $36 Bn. i.e which is an increase of 5 x in 2 years and perhaps rightly so because it impacts everyone. This has allowed innovation and flexibility in financial services. With the cross domain application capabilities of big data and machine learning, it has in itself surrounded the main business model of plenty such startups in FinTech especially in the cyber-security domain. In the coming years it is expected to play a major role in the same with its application expanding in Predictive Analysis for Credit Scores, Fraud Detection, Identity Management, Trading Algorithms and Information Extraction etc.

However, advances in machine learning to ensure cyber-security in FinTech also makes it vulnerable to attacks that are based on Machine Learning algorithms. Not only that, it would make it hard for the system’s human designers to understand system behavior and it’s associated security weaknesses.

This scenario resembles an arms race where machine learning has made its entry and increased the complications. This puts the system designers in a spot when building machine-learning based platforms as they now need to pay particular attention to the biases that could be inadvertently built into solutions and thus create cyber-security vulnerabilities by themselves.

 

 

Link – http://www.forbes.com/sites/johnvillasenor/2016/08/25/ensuring-cybersecurity-in-fintech-key-trends-and-solutions/#73fa9cc8e1fa

It hasn’t been long ago since the time when companies actually realized the importance of Data when making decisions. It is rare to image a time when executives from the top management of a company met and did not discuss the company’s finances based on the data. But here lies the problem with this data globally – IT IS INCREASING EXPONENTIALLY. And the systems that we have been using till date to store this data (with no hard feelings) are simply incapable of matching the rate of data generation. And any FinTech is no exception to this.

Sure the Data Warehousing and Business Intelligence is well supported by the age old Relational DataBase Management Systems (RDBMS) but with that came the problem of Data Redundancy and Data Flexibility questions.

I feel that, moving forward, for newly formed organizations or those that plan to expand their reach [and expecting to generate terabytes of data in the future], it is important to consider making changes at the grass-root level for adapting the technology that employs the use of In-Memory-Databases. Sure they come with a price and a good one too but it could certainly be considered as a one-time investment that will bear them an ability to reduce the overall Data Volume without compromising on the performance and great data flexibility.

When we refer to ‘Open Source’, we usually stereotype it as something related to software (say Android or Python based application) especially when the context relates to a Financial Information Systems. However, Mozilla (the company behind the Firefox web browser) has managed to break this stereotype by making a low end smartphone that could prove to be a promising instrument in the world of finance based systems. Similarly, Allevo (a Romanian company) has put in a core banking transaction processing software in the market while UK’s OpenGamma is providing a risk analytics platform (a hot topic in a banking/ finance institution). But what does it take to make such a technology work ? There are a few must-haves for a financial system that aims to leverage open source technology –

• Accessibility
• Outreach for the Customer (using a Know Your Customer [KYC] system)
• Flexibility
• Open (should be based on Open APIs for new service providers)
• Complementary (Ability to combine services as needed)

But building an ecosystem adhering to the above stated requirements and expanding it across borders will take some effort even more so because the core requirement for this technology is a system that is – Open, Safe and Reliable.

Full Article – http://bankinnovation.net/2014/04/four-technologies-that-will-revolutionize-financial-services/

An organization trying to incorporate better financial information systems into their daily operations, need to understand the importance of Transaction Control and its roots lie in the very basic way of making a business transaction. Much of the failure despite huge investment in a financial information system is due to bad tracking of the cash flow in and out of the company. Sure, prior to online internet based transactions, exchange of cash against resources was the only legitimate way a transaction and was considered complete but with companies scaling their operations to reach across borders (both locally and internationally) and also in size, it is important to understand that tracking of cash flow is anything but easy.

By switching to cashless transactions, the cash flow can be monitored in a structured manner and the workflow can be set to adhere to various compliance requirements and approvals before a transaction can actually be considered complete. 

Switching to a cashless environment involves great efforts but is sure to complement the objective of having a Systematic, Secure, Integrated, Digital, Automated and Standardized Financial Information System.
Link – https://jfin-swufe.springeropen.com/articles/10.1186/s40854-016-0023-z