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1、ADVANCED ANALYTICS IN BANKING Introduction 1 Current business challenges.2in banking industry How analytics manage these challenges.3 5 real world 10 xDS RPA use cases.4for analytics in banking10 xDS analytics practices.9 Conclusion.10About 10 xDS.11CONTENTSIntroductionFinancial institutions will be
2、 facing increasing challenges dealing with the changing compliance rules and scrutiny by public authorities,law agencies,statutory/regulatory bodies.Traditional and legacy tools are often cumbersome and time consuming to use for gathering and analyzing the data for business insights.Today,with sever
3、al advanced technology options available,significant number of organizations are investing money and efforts into unlocking their huge potential of data.The first step in the transformation pathway for building analytics capabilities begins with employing descriptive analytics techniques,which helps
4、 in reviewing the past performance of the organization.It further helps in understanding the hidden patterns that provides insights in terms of customer behavior,sales trends,product performance,risk patterns and others.Analytics offers key information needed to improve operational efficiencies and
5、boost profits,reduce information bottlenecks,greater customer insights and better decision making.In order to identify and act on risks in advance,advanced analytics techniques such as predictive modelling,machine learning,and forensic analytics are very effective.This also enables organizations to
6、have more powerful tools and technologies to predict sales trends,create what-if scenarios,identify and act on customer sentiment,segment customers for customized product promotions and so on.1Current challenges in Banking Industry Banking industry faces some really pressing problems such as:Difficu
7、lty in differentiating fraudulent interactions from legitimate business transactions.Difficulty in understanding customer behavior to push the right promotion and marketing campaigns.Lack of clarity in the portfolio risk status,which in turn has an impact on the bottom line.Lack of information regar
8、ding ways to find out new sources of growth or opportunities.Lack of key information and timely notifications needed for banks to meet compliance and regulatory requirements.2How Analyticshelpsmanage these challengesA.CUSTOMER ANALYTICSCustomer Analytics can give organizations the ability to recogni
9、ze,attract and keep the most profitable customers with the help of processes and technologies providing key customer insights for decision making.Here are some of the key areas:Sentiment Analysis is a method used to monitor the conversations,language and voice inflections in order to calculate custo
10、mers emotions related to a business,product or brand.Segmentation is the process of dividing the customer into groups of people that are similar such as age,gender,interests and spending habits.Lifetime Value predicts the net profit related to the entire future relationship with a customer.Customize
11、d Promotions involves those promotions that are targeted to certain customers in the society.Cross sell/upsell is a way in which one persuades an existing customer to buy something additional which is comparatively more expensive.Customer Churn:is the estimate of people who stopped using the product
12、 of the company,in a certain time period.B.FINANCIAL ANALYTICSIt is through financial analytics that the organization gains insights on different views on the businesss financial data.Here is how:Financial Risk Analytics provides banks and financial institution with better understanding of customer
13、profile and insights that enable them to anticipate customer behavior.Leveraging risk analytics techniques,banks can save their time,money,and resources to target right customers and monitor or anticipate the risk involved.Application Screening helps in better screening of customer applications to a
14、ffirm legitimate ones that can then be fast-tracked to approval for a more positive customer experience.Analytics can ensure more authentic results in the screening process to gain good customers,and the bad ones are detected before they join.Sales and Service ChannelOptimization offers banks key in
15、formation for identifying tactics for each customer to effectively align and optimize service channel usage with customer and bank preferences.35 real World10 xDS use Cases for Analytics In bankingHeres a glimpse of some of the Analytics solutions we have delivered for the Banking industry.1.Operati
16、onal Risk DashboardIn the absence of a centralized data platform or when there are multiple entities and business units spread across different locations,using different tools and following different technology stack to store and process data at various levels,the effort and time required to collate
17、 and report the risk data across various sectors can be highly inefficient.There are times when the leadership has no way to view the data from multiple perspectives to get insights.This is where a descriptive analytics solution such as an Operational risk dashboard can prove to be very useful.The s
18、olution offers a web-based view of the risk parameters helping the leadership to monitor the risk KPIs periodically and understand their trends.Analyzing the trends,the leadership can device strategies for mitigating upcoming risks.Value add:Insights into risk data with drill down,filtering,slice an
19、d dice facilities.Efficient process to consolidate and refresh the risk dashboard periodically.42.Forensic analyticsThere is an overwhelming need for a technology platform to review the risks and monitor the transactions as banks and financial institutions are increasingly scrutinized by the public
20、due to occurrence of frauds and wrongdoings.In order to minimize its impact on the brand and its value,more importantly to its customers,we use advanced analytics techniques in order to learn and understand the past fraud transactions.Also advanced modelling techniques could be used to build a machi
21、ne learning based predictive model that foresees the probability of any fraudulent transactions,thus minimizing the risk of its occurrence and improve the brand image and customer retention.Value add:Better management of fraud.Improvement in brand image and customer retention.53.Predictive maintenan
22、ceFinancial institutions spend a lot of money and effort on maintenance ofATMs.Organizations could save money by reducing such operationalcosts if they had insights into which ATMs are nearing maintenance,thusenabling them on better utilization of the maintenance staff and reducingoperational costs.
23、In this case advanced analytics and machine learningtechniques can be used to predict the probability of ATM failure.Toenable this,past data needs to be consolidated from past failure logsand other sensor readings of various components of the ATMs.Predictive models could be utilized on regular inter
24、vals to get theprobability of the failure of the various ATMs based on the recent datacollected from these machines.Value add:Better utilization of maintenance staff Reduction in operational expenses64.Application screeningNon-Performing Assets(NPA)are a burden for any financial institution.To reduc
25、e the risk of any asset becoming an NPA,to understand the customer repayment power,probability of default and for assessing various parameters,banks need to use various controls/measures at different stages.In such a case,predictive modelling and machine learning techniques can be utilized to create
26、 a model which accepts the customer details and predicts the probability of the customer defaulting the repayment.Bigdata technologies could also help in building an efficient screening process,which processes massive data volumes including inhouse,social media and publicly available information abo
27、ut the customer.There are several other technologies available to keep track of the customer and provide alerts at the earliest possible time.Value add:Better assessment of repayment capability of a customer by looking at various parameters which is impossible via manual screening Reduction of the p
28、robability of an asset turning into NPA75.Customer AnalyticsBanking institutions are applying innovative ways to increase the number of financial products per customer which would ultimately improve their customer retention and profitability.However,executives are finding it difficult to identify an
29、d sell the right product for the right customer at the right time.In order to solve this,advanced analytics techniques can be used to combine big data sets like customer demographics,key characteristics,products held,credit card statements,transactions and point of sale data,mobile payments and data
30、 from credit bureau.This approach would help classify the customer base and identify similarities and create micro segments among the customer base.Value add:Creation of customer micro-segments Improvement in the effectiveness of product campaigns810 xDS Data Analytics Practice10 xDS Data Analytics
31、Practice10 xDS is driving digital transformation by leveraging our expertise on data and analytics technologies and deep sector experience to enable organizations make effective strategic decisions.Our strong portfolio of data and analytics services includes Information Management,Big Data,Business
32、Intelligence and Advanced Analytics solutions for assisted and semi-automated decision making.1.Information Management SolutionsThis comprises of Enterprise data warehouse,Data modelling,Data integration,Data migration and conversion,Data quality and Data governance.2.Business Intelligence Solutions
33、This consists of Enterprise reporting,Report migration/enhancements,In-memory analytics,Multi dimension analysis and Mobile BI.3.Big Data Solutions10 xDS Big Data solutions help organizations to better analyses customer behavior,make better business decisions,test new products,manage relationships,a
34、nd build customer loyalty faster and with more confidence.Our big data solutions include-structured/semi structured/unstructured data analytics,social media analytics,streaming data analytics,Hadoop based solutions and,Cloud enabled services.4.Advanced Analytics Solutions10 xDS Advanced Analytics so
35、lutions help organizations gain actionable insights by applying machine learning,predictive modelling,advanced visualization,statistics,and other advanced techniques to diverse data sources.910Analytics solutions are quickly transformingthe banking world,improving profitability,compliance,competitiv
36、eness and helpingshapebusinessdecisions.Developingbanksanalytics capabilitiesmay seemdaunting at first but you can start with smalldoable steps,integrating data analytics intooperating models and expand from there tostay ahead of the competition.To learnmore about how to kickstart your analyticsinit
37、iative and efforts to enable smarterdecision-making.Talk to our experts!CONCLUSIONExponential Digital Solutions(10 xDS)is a new age organization where traditional consulting converges with digital technologies and innovative solutions.With an expert team having diverse experiences and backgrounds,10
38、 xDS is committed towards partnering with clients to augment their performances and help them realize their most important goals.We do this,by harnessing a blend of automation,analytics,ai and all thats“new”in the emerging exponential technologies.Today,we are treading an exponential path of growth,delivering many successful and transformative engagements,designing and creating new business models,business advisory partnerships and center-of-excellences!More importantly,we are building an unassailable trust and confidence from our customers,partners and employees.ABOUT 10 xDS