Commentaries and insightful analyses on the world of finance, technology and IT.

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May 26, 2016

Social trading is redefining trading

Thanks to bionic advisors, my investments (better read) as long-term wealth is taken care off! But who doesn't want some quick profits, higher returns? By now, you would have guessed that I am talking about stocks, stock trading, to be precise. Here's my stock trading story. Before putting my money in stocks, I decided to seek guidance from my colleagues who have been trading for decades now. Unfortunately, they weren't interested in sharing their strategies with a debutant trader. So I began researching on the Internet to understand the stock markets better and that's how I stumbled on an interesting new concept - social trading.

In a nutshell, social trading is a mechanism of bringing together traders across the globe into one big network and providing traders the option to leverage trading techniques and strategies of other traders. Unlike the old school of thought on trading where trading strategies were closely guarded secrets, this new trading concept allows traders to follow or even blindly copy trading strategies of top investors.
Social trading is being considered as the next big phenomenon in the capital market. The social trading platform is a vast ocean of information, available for free for any trader. The perpetual data flow enables traders to make profit out of trade even if they don't possess any erstwhile information or experience on trading. Thus, it seems to be the best choice for traders especially, novice traders. And it helps the top investors get some extra pounds by sharing their trading skills.

On realizing that social trading is the best option for me, I started looking for a viable social trading system. Interestingly, I found that fintechs are extremely active in the social trading space. Though investment banking had been sort of opaque to fintechs, the rising wave of social trading is seeing lot of traction from fintech firms such as ZuluTrader, eToro and StockTwits. Currently, ZuluTrader is the largest global social trading network with the highest number of traders and investors. StockTwits, on the other hand, is a social communication platform that uses tweets for trading while eToro aims to help novice investors. Other well- known players in this space are Ayondo, Tradeo, SignalTrader, and more.

I glanced through a few of these leading platforms and found that the instruments traded on these platforms are quite widespread, covering stocks, forex currency pairs, gold, silver, commodities, indices, oil, etc. These platforms are quite user friendly and most of them include a live feeds feature, giving higher visibility on the trading operations of all traders. After viewing the feeds, based on my choice and requirement, I could "follow" the top traders / investors. Then if convinced, I could blindly "copy" the trades of those leading investors - essentially, allocate a portion of my funds to the selected investor / trader and their trades gets automatically copied, making my life easier and richer. Interestingly, some of the solutions even enable the traders to get in touch with the topline investors directly to clarify their concerns.

Social networking worked and is thriving, will social trading see a future? Actually, a lot of action is already happening in the social trading arena - many firms, especially, fintechs are entering this dynamic and active space. Firms like Ayondo are spreading its wings to growing markets like Asia, local regulators across the globe are showing profound interest. All this indicates that social trading is bound to stay and would definitely be the resort for the tech-savvy millennial who prefers fast and smoother trading experience. However, the sustainability of the concept depends on how well the knowledge of traders can be applied or tweaked to the likes of the other traders.

May 23, 2016

Analytics is the oxygen that energizes new banks to scale new heights of modernization

Large, global banks process billions of transactions across service offerings to a plethora of customers across demographics, daily. In order to sustain effective operations, they must adopt cutting-edge analytics that churn the petabytes of rich information into valuable insights.

As of today, most global banks are processing these petabytes of transactional data through legacy and modern databases that get downgraded through years of mergers and acquisitions. Therefore, migrating complete legacy and distributed data towards a robust storage solution that addresses cur-rent challenges and future requirements, marks the first step towards modernization.

Having said that, banks also have to make sense of two data formats -- unstructured and un-leveraged format from legacy databases, and structured data from new tools in big data and analytics. Towards that, they must implement solutions that center on R, Python, SAS, or NoSQL driven analytics. Not only do these solutions integrate structured and unstructured information, but also process it like a fast moving Pac-Man! In fact, they produce unbelievable outcomes, occasionally influencing strategic outcomes and are mostly open source. At the same time, Blockchain technology is a new kid on the block! To maximize value from the opportunities that Blockchain presents, banks require top-class analytical and data processing capabilities.

Therefore, business analytics is an invaluable capability for organizations. It augments competitiveness of service offerings, market growth, and relevance from the current perspective. Simultaneously, cog-nitive / predictive analytics, which has been neglected for quite some time, is equally important to en-sure anti-money laundering (AML) / fraud detection. For a considerable amount of time, banks have overlooked the hazards of incomplete and missing information across Know Your Customer (KYC), Know Your Employee (KYE), Customer IDentification (CID), Customer Due Diligence (CDD), and En-hanced Due Diligence (EDD) processes, while driving competitiveness. Today, such negligence can prove costly and I can extend the point in discussion to Cash Management services which Banks ex-tend as a value-add for a small fee. In the current scenario, cash management teams must focus atten-tion towards cognitive / predictive analytics for AML / fraud detection. This is because these teams directly handle cash coming from external sources, which could be honest or obscure with a dark un-derbelly. Consequently, services that cater to cash collection, dropbox, vault, sweeps, zero balance accounts, and cash concentration are abused. This is because some of these overlap with the realms of private banking, which hide the true beneficiary behind the wraps of secret arrangements and agree-ments.

In my next blog, I will discuss about how applying R, Python, or SAS on the available 'structured' infor-mation combined with available 'unstructured or free' information will come handy towards harness-ing the power of fast and efficient analytics, using underlying legacy and modern silos.