Big Data analytics in private banking & wealth management

and Keith I think they bit touch upon you know this big data analytics and the height which is there around big data what I'm going to do is I'm going to give some perspective in terms of the reality check you know what's really happening in in the Wealth Management and private banking space so financial industry is huge and and there are specific challenges with respect to each and every vertical of financial industry so it's very important to understand what what actually is private banking so let me start from there and then you know we will take a deep dive into the reality around Big Data in private banking and wealth management space and and what are the realistic use cases from the perspective of advanced analytics and and usage of Big Data okay so what is private banking private banking is a relationship driven business with high net worth and ultra high-net-worth individuals it's it's not like our normal retail banking operations wherein you have credit cards and you go to the branch and you transact it's a very specialized and personalized kind of service that banks offer to to individuals who are pretty busy they are looking they have a particular risk appetite and they're looking for offerings and suggestions from from the banks to invest their money and to manage their money and wealth yeah so that's private banking so every hnw high net worth individual or you H&W and ultra high-net-worth danger individual they are allocated with the relationship manager and the relationship manager basically advices on the basis of what's really happening in the market what is the risk appetite of that client and so on now that's the current model now talk about the future model how you can translate this whole kind of process into something which is more big data oriented now let's imagine that there are there is a lot of information flowing in the market there is live feeds coming there is information about the industry across the region there is also information of a level because of mobility about the particular client as well if somehow there could be a system in an integrated system which could host all this data together can make sense of that data and can give a client specific recommendation to the relationship manager that for today these are the five different advices if you will offer to the client then the chances of client really going ahead and and you know abiding by this and gaining profit out of it are high and as a company as a private bank we will also gain if we enter into these high conviction offerings well this is not a dream this is really happening iBM has come out with the supercomputer called IBM Watson and they are kind of projecting this as a next species of a big data recommendation engine it has already been tested in healthcare and I think wealth management and private banking are the next users where in this super computer can kind of take in all the data from multiple sources we made structured or unstructured in nature can make sense of that data and can give clients specific advice which could be offered back to to people you know to the high net worth individual clients so that's just one use case and something like that is what I want to share in this presentation so big data is nothing but it's it's it's a set of processes technology and framework which could be used to not only make sense of the structured data in the audience form that is there in the in the company or in the bank but also to assimilate the signals which are coming out of social media channels like Twitter's or the word or Facebook or LinkedIn and then try to integrate this with the structured data that you have Plus this unstructured data now why we call it unstructured because data like this cannot be directly put into a we set schema or relational database form and that's why you need to have key value appearing basically to integrate this data together and associated technologies so that's a very layman or high-level view about what this big data are kind of mesh out is and there are associated technologies to support this what Big Data can do for wealth management or private banking firms it's very simple in today's scenario CIOs they are dealing with three main priorities number one how we can get more share of the wallet from our existing set of customers how we can integrate and and can have better communication channel with our customer how we can understand their behavior better so this is not all about leveraging what you already have in the system this is about leveraging and gaining more insight from what they're doing socially you know in facebook if they are really talking about going to a particular place can we offer them a piece of land there you know any offering which which can make sense to that particular customer so first priority is how I can increase the revenue how I can get more closer to the customer and obviously cross-sell and upsell in terms of the business which I already have with them the second priority is how I could get efficiency into my existing set of processes so you know systems are already there there is not even a single single bank which doesn't have bi infrastructure but then the banks are struggling because they may not have the operational operational efficiency drive going into the system which is basically to see where are the bottlenecks and how we can get rid of those bottlenecks by having a more intuitive and robust process in place which is which can reduce the TCO for for the bank third most important priority for any CIO right now is minimizing and optimizing the risk exposure and this could be in the credit risk side of things while onboarding a new client it could be on the market risk or on the operational which every bank is kind of exposed to so these are the three main priorities and on the basis of big data team as I think Keith also mentioned your business case should drive the technology technology decisions and should not be other way around and that's why if you tie up these three priorities the four main spheres wherein you can have a big data case in the private banking division is on the customer needs site having a dynamic profiling of the existing set of customers and we call this as a customer intimacy concept and of course for that you need maybe more data than what you already have and the associated technologies and the second is coming out with a big data recommendation engine engine which is intuitive in nature which could digest the data coming from market feeds coming from internal sources external sources social media and can give you can analyze that and can give you four or five bullets this that's what as a RM I should be doing and and should be talking to you know with the client second one I already kind of talked about operational intelligence then the third one in the green section you see is detecting the risk and checking the fraud this is really really important in the private banking and wealth management business one of the key thing which regulators always look for is have you done due diligence while onboarding a new client now what I mean by that is when the reputation of a company goes for a toss if you onboard a client which may be indulge in AML nd money laundering activities and that's why every private banking client has to be you know it should go through the whole procedure of enhanced profiling as well as onboarding procedures what private banks are doing they are making this whole procedure very robust and dynamic they are integrating and taking and assimilating all the data coming from Google different data sources news databases and the verification at the time of onboarding is done through the analysis done on this big set of data which could be unstructured in nature and that's why we call it big data I will talk about outlier tooling as well that what we actually mean by that so if there is a pattern which was not there and is emerging out on the analysis of the data which means a red flag should be raised and and something should be done about any unusual transaction in which business or or your client gets into so there is lot of attention in that area as well and then of course the fourth one is the data warehouse optimization with Big Data technologies as I said you know all the companies all the banks they have Big Data Platform they have different tooling around it for doing the analysis but at times what happens is they are not basically reviewing what they already have and any data if it is not used for a long period of time they don't archive it it still sits in their code system they don't use you know the available technology like Big Data for kind of parking it into a place which which could give them cost efficiency and and that's also one use case I see which Big Data can provide ok so this is again to get more information around customer intimacy now what you really do in customer intimacy again it's a term it could be any other term as well it's about dynamic client segmentation every bank they they do the segmentation of the client on 4 or 5 main criterias but I think dynamic client segmentation is kind of going to a step ahead wherein on a dynamic basis you do the segmentation of the client on the basis of their behavioral pattern their transaction pattern and you create multiple segments maybe 80 or 90 different segments of customers and then you associate those segments with the products that you would like to offer the private banking products that you would like to offer by doing that since you have invested in terms of analyzing the need of the customer the hit ratio tend to go high and that's what really banks are really investing into and big data recommendation engine basically goes hand in hand with that when you have done the segmentation you need to eventually then offer a product which this client will really get into a buy or invest into and that's where this big data recommendation engine helps in managing and merging the segmentation of the client with associated product which one should offer to them operational intelligence now this is very important normally banks or any any industry they will have lot of data lying in logs and machine data which is just kept in the logs but nobody is really analyzing it and now we have tools likes plunks of the word or log le which can read and make sense of this data to identify if if people are you know if the genuine people are really kind of getting into the system which are the processes taking more time that is also one information you can get from log now manually also you can kind of identify those lots but with the big data tools like Splunk automatically spun can use indexing mechanism to get into your logs or machine data and again can give you analytics which is easily understandable and once you know what the problem is there could be horses for courses you can you can straightaway get to the solution of that aspect I think the main thing is diagnosing what really is the bottleneck in your system and that's where Big Data technology can really help on the other side you see I think I already talked about data warehousing optimization with Big Data technologies so any data that you believe is not really used in the current set of analytics and process that's where big data platforms like Hadoop they're many kind of distributions enterprise level distributions in the form of cloud era map are and there are many boots outside as well and at the end of the presentation I'll also give you a view of the Big Data landscape which is again not technology specific but generic landscape which one could use to kind of reduce the cost by leveraging the power of Big Data technologies last and the most important bit is detecting the risk and checking for fraud you know for all the bank's billions of dollars they really pay if they are not compliant with the AML procedures if they're not compliant with the regulatory bodies and that's where the preventive action could be taken by by having an integrated data set or an engine which could which could give information about what are the potential red flag and again as I said once you know if the suspect you can do something to kind of cure that disease as well now at the end of it this is what the Big Data landscape at a high level looks like you know new companies are mushrooming up and this is changing on a daily basis but this is one time in place it's about kind of creating the layer at the bottom and then having different verticals and horizontals on top in terms of infrastructure in terms of the analytical enterprise level distribution capability that you would like to have in terms of analytics and visualization that you would like to have the BI tooling that you would like to have the good news is some of these tools we all have you know in our organizations it's just about leveraging and integrating it with the Big Data technology so that you can really make use of the technology which is which again should be associated with the with the business case cloud has a role to play in this as well you know if you see as a service window Azure and Amazon Web Services they do offer big data offering you know for for cost efficiency and for for you know as a service so I think in the end my my closing remark is in in the retail banking side and in the insurance side definitely there is a case of leveraging big data because the number of transactions and number of clients with which you deal are definitely more but then it's slowly getting into private banking and wealth management space as well but as I said the key is you should have a business case and then you should look for the technology which could solve the purpose for you thank you very much for your time

One Comment

  1. K G said:

    Thank you very much for uploading.. if only we could get a clearer view of the slides!

    June 26, 2019

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