TechFocus
Business intelligence for banks
Bangladesh has experienced a plethora of internationally accredited banking software being implemented to a few leading banks. Two objectives stood out for such a mission critical endeavour, which involved cohesive strategy, aligning business units, stakeholders and technology group. The perceived success was driven by the vision of stakeholders and zeal of management, in reaping benefits from technology. Firstly, stakeholders wanted to introduce a new dimension to banking through cutting-edge technology. This brought in new policies and procedures, through effective identification and implementation of change management. Secondly, management wanted to benefit from technology-driven products and services. The objective was to offer better customer services. Hence, the Customer Relationship Management (CRM) portfolio embarked on new Key Performance Indicator (KPI) metrics. The key to embarking on a Corporate Data Warehousing (CDW) and Business Intelligence (BI) project is to ensure that operational data reside on a single platform, are characterised, have a time series cascading of presentation, allow aggregation, and overall, ensure consistency, accuracy and 360o insightful information for improved decision making. Information is Knowledge
Traditional analysis demanded raw data to ascertain business trends and profitability analysis. While BI emphasised on the nature and life cycle of data. Obvious question in a data-driven institution is not 'how much will I accumulate from my newly launched DPS product for low cost of fund?' but 'what do I have to do to ensure that my new liability product accumulates an astounding figure of 22 Crore by 7 months?' The iterative process starts by looking at the knowledge stage of data and going back to the raw form to provide an assumption. The ability of this predictive analysis yields better results for business. A recent survey by the PMP Research in the UK revealed the top two reasons for using CDW, based on a scale of 1 to 5, where 1 indicates 'not important' and 5 'very important', are to (i) improve the quality of decision making (4.5) and (ii) to increase the accuracy and integrity of their data (4.3). Why build a data warehouse?
Banks can augment competitive advantage and profit by harnessing corporate data as a strategic tool. One objective should now be to introduce users to a new BI system, which can be used to meet business goals in particular how it can make their work lives easier and more productive. In doing so banks should now focus on identifying information stewards across the business. Finance may want to build hypothetical models leading to 'what-if' analyses. These, then, provide the ability to predict an unknown opportunity. This exercise is particularly handy while reacting to changes occurring in a fiercely competitive marketplace. Jack Welch, the flamboyant ex-CEO of GE once said "If the rate of change outside of your organisation is ever greater than the rate of change inside your organisation - it's over". 1. Customer Relationship Management (CRM)
Key targets for CRM initiatives are to provide business intelligence to achieve bank's strategic objectives, which are attracting, servicing and retaining customers. But first, what is a CRM strategy? Key areas to focus while building a CRM Decision Support System are: i. How many customers do you have and who are they? ii. Who are the most profitable customers? iii. Which customer segment delivers the largest revenue? (Pareto rule: 80/20 analysis). iv. How many different product ranges does the customer buy from you? v. How loyal is your customer base? vi. What is your customer churn rate? vii. What proportion of the customer's wallet do they spend with you? viii. How many customers repeat availing funded or non-funded services? ix. What is the external perception of your bank? x. Which business area generates highest customer complaints? xi. Do you know how to generate cross-selling, or up-selling or down-selling with your selected customers? The above questionnaires then provide a basis for CRM KPIs for effective analysis of information, leading to operational efficiency and profitability. 2. Central Bank Reporting
A data warehouse feeds the build of CL, SBS, CTR, STR or Forex related returns. Attention should be given to data extraction, transformation, cleansing, and population from operational data. Banks may want to build reports based on pattern of fraudulent activities. For example, what transactions usually take place on money laundering attempt? Once such a pattern is established, it is then easier for banks to replicate the concept on other key areas like customer churn analysis. 3. Asset Liability
Management
Treasury can take great advantage from data modelling, and the hypothetical analysis derived from such modelling. A relational or multi-dimensional model should allow real-time "alert" on the cross-functional impact of 'interest and expense'. Driven by a rule-based analytic, this model can quite nicely prompt business on interest rate distribution matrix, for funded or non-funded products, for a variety of interest rates, on liability products. Such ability allows Treasury and Finance to predict a 'balanced scorecard' in view of maximising profit by deriving better yield on interest spread. 4. Credit Approval System
An online credit approval system reduces a lot of operational redundancies and improves efficiency manifold. It allows building an online repository of customer balance sheets and allows stamping credit information received from the Central Bank. The credit approval system allows developing KPI Metrics on how you want to measure your operational efficiency. The challenging part lies in centralising pertinent business processes by a specialist group called BPR. 5. Operational Reporting
Operational reports from a data warehouse may include loan listing, balance sheet analysis, expense & income GL analysis, daily position etc. Banks should segregate hosting of online data from a data warehouse. A dedicated server is recommended for hosting such analytical data, which should be updated during the evening, after completing end-of-day processing, through a well-designed Extraction, Transformation and Loading (ETL) tool. Power users within each business unit should be trained on structured query language (SQL), reporting tools like Business Objects and data modelling. They should be able to design and develop their own queries, leaving core IT members to get along with more optimisation work and support. They are the 'knowledge-base' within the banks. A CDW helps to build a dashboard or Enterprise Information Portal (EIP) for senior management, moving away from bottom-up-reporting to top-down approach, drilling down to details, when needed, with slicing and dicing capabilities. The benefit allows pivoting corporate data from a new perspective, which is not possible with traditional column-based reporting. While a CDW ensures 'one version of the truth', an EIP ensures that senior managers make the best usage of their IT assets, both information and hardware, in reaping true benefits of technology. Conclusion
The recent trend of adopting top-notch banking software in local banks has put them in a linear competition of banking facilities. The ones who would be able to transform the pattern of building quantitative data, measurements and figures into qualitative extractions and knowledge assets will ultimately lead. However, without an Information strategy you may end up having lots of data and no information to base your decision.
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