Finacle Blog

An analyst firm's report on the mobile internet says that in 2014, mobile trading-related activity would be 10 times that in 2009. Another investor survey revealed that a significant proportion of respondents - especially among those below 50 - wanted multiple options to connect with their financial advisors, including mobile texting, tablets and video conferencing.

Taken together, these facts indicate the potential for mobile wealth management services, something that smartphones have helped exploit. When financial services (including wealth management) were first offered over mobile, they were limited in functionality and hampered by the inconvenience of text-based messaging. But smartphones changed the game, with their user friendly interfaces, device agnosticism and a huge range of downloadable applications to fulfil every demand - whether for trade order management and stock research by customers or financial planning, CRM and portfolio analytics by their advisors.

Thanks to smartphones, mobile wealth management is now:
• Intuitive, feature rich and well-rendered on any device
• Versatile, enabling customers to perform a variety of functions from account aggregation and research to financial planning and trading
• Extremely interactive, allowing users not only to connect to their advisors through email, voice and video but also with friends over social networks

To bank advisors, smartphones have brought a much needed unified view of customers, and access to enterprise applications. These devices connect seamlessly to other banking channels ensuring that the interactions on each are available to wealth managers on demand over their handsets.

Today, with the demand for investment management services spreading to the mass market, financial service providers need a cost efficient way to reach out to a huge number of customers, which they never had to do when they catered to a niche, high net worth audience. Smartphones are the answer.

Posted by Sai Kumar Jayanty at 9:09 AM | Permalink | Comments (0) | TrackBacks (0)

I have lost count of the number of surveys reporting the dominant influence of social opinion in decision-making. Today, almost every website features a link enabling visitors to 'share it' on Facebook and other popular networking platforms. Suffice it to say that consumers are starting to rely a lot more on the advice of their social communities than on company-sponsored communication while buying a product or service.

The telecom, automotive and retailing industries are among the leading users of social platforms for customer engagement. Take a look at the Amazon site and you'll see what I mean. However, the same cannot be said of banks, which due to their natural conservatism and regulatory concerns, are still waiting and watching, barring an adventurous few that have started to feel the pulse of this phenomenon.

I think market dynamics - defined by consumers' increasing use of social media especially on their smartphones, the maturation of social technology, and the evolution of a regulatory framework for social transactions - will propel banks to change their attitude sooner rather than later, starting with the early adopters who will render social media participation ' hygiene' in the years to come. And that's good news because in the long run, the adoption of social media can bring them many benefits such as a chance to participate in the consumer decision-making process and build advocacy in their bank's favor. Also, here lies an opportunity to improve sales, service and customer experience.

Banks can also get direct feedback from customers on what they like about the bank and its products and what needs improvement. Co-creating with customers to come up with better, more relevant offerings provides a fillip to innovation and product development and, exposure to real-time customer information can be used to propose offers on the spot, customized to the immediate context. Moreover, the channel offers exposure to new opportunities such as P2P lending and a chance to spread financial awareness by contributing to discussion forums and providing access to Personal Financial Management Tools over social media.

A clutch of forward-looking banks including Bank of America, Wells Fargo and Citibank, have taken a head start on their rivals in the social arena. It is time the rest played catch-up, if they are to leverage any advantage of being early starters in this game.

Posted by Chandramouli Kundagrami at 8:58 AM | Permalink | Comments (0) | TrackBacks (0)

My last post talked about the factors dragging the adoption of Predictive Analytics. This one is about jumpstarting it.

Organisations have too many fears with regard to predictive analytics - can they handle it, can they afford it, will it work... etc. They need to dump this baggage. In truth, there are several ways to embrace predictive analytics, some simpler than expected. Rather than get bogged down by the enormity of the task, organisations must take a level-headed look at their processes and available data - which are the keys to determining the actual solution.

As mentioned in my previous blog post, statistical analytics experts are hard to come by. But they're not the only ones for the job. Research shows that companies have successfully deployed a combination of IT developers, business analysts and users into their BI projects. Only one in two hire specialist analytical modellers. This shows that the unavailability of highly skilled resources need not be a stumbling block. In fact, multi-skilled users bring a distinct advantage of balance, because they will likely recommend a mix of processes and tools that are suited both to regular as well as 'power' users.

The success of predictive analytics hinges on the quality and availability of data. Hence, it is important to assess data availability and associated risk at the outset. Here again, the use of a multi-disciplinary team can improve the accuracy of data validation and eliminate the bias that usually creeps in when only a single analyst is entrusted with the job. Through these measures, the organisation can streamline its sources and quality of information well in advance, rather than grapple with issues midstream.

Having realistic expectations is equally important. Analytics is a long term game, which can yield handsome rewards to those investing in it. Organisations must therefore look at the big picture rather than quick wins to avoid being sidetracked. A vital part of this exercise is correctly defining the business problem which predictive analytics is meant to solve, and making sure that the latter is indeed the best fit for this situation.

The good news is that vendors are doing their bit to ease adoption of analytics solutions. They have developed plug and play applications with rich, customised functionality that are an expedient and cost effective alternative to building an in-house solution from scratch. Many vendors offer open specialised solutions which can be built on to existing packaged products as a differentiating element. What's more, many of these solutions are hosted on the cloud, and made available to users on a 'pay as you go' basis, thereby reducing their initial investment significantly, as well as divesting them of the burden of maintaining the solutions in-house.

Posted by Balwant C Surti at 9:41 AM | Permalink | Comments (0) | TrackBacks (0)

So far, the story of Predictive Analytics has been one of unfulfilled promise. In this blog post and the next, I'll talk about the reasons for its slow takeoff and ways to accelerate adoption.

One of the things holding back predictive analytics is a lack of skilled resources. Working on a predictive analytics model is a specialised job that only statisticians can do, and they are hard to find. However, a lot of surrounding knowledge comes from the business domain, which can be leveraged by business users with the help of technical experts.

The second barrier is unavailability of good quality past data, essential fodder for any business intelligence solution. Where data is not readily accessible in the desired format, it may be necessary to extract it from the organisation's data warehouse - most have them these days - or in the worst case, pull it off their operational systems.  Painful no doubt, but well worth the effort in the long term.

Today's businesses are information intensive and highly variable - this means that setting up an analytical model takes time and effort. Packaged third party offerings are emerging as an alternative to an in-house ground-up solution, and are worth considering for their expediency.

The other challenge to the adoption of predictive analytics is its slower payback. Predictive analytics delivers solid returns, but over a period of five years or longer and more so on big-ticket (read big investment) projects. This puts off many organisations lacking the necessary resources, risk appetite or patience. This is also the reason why pilot projects don't get the visibility they deserve - the bigger ones take too long to show results and the small ones don't show enough. One way to solve this problem is to involve business users right from the start, rather than leaving it all to a parallel team.

My point is that predictive analytics - like any technology - has its issues, but there are ways to help it along. I'm leaving that for my next post.

Posted by Balwant C Surti at 6:55 AM | Permalink | Comments (0) | TrackBacks (0)