Reference no: EM133055727

Enabling Strategic Decision Making: The role of Decision Support Systems in banks 

Two friends hiking in a forest suddenly spot a tiger. One of them quickly reaches into his backpack and takes out his running shoes. His puzzled pal asks, 'Do you really think those are going to make you run faster than the tiger?' 'I don't have to run faster than the tiger', says the first guy, tightening his laces, 'I just have to run faster than you!'

How often are we prepared for situations where we must make critical judgements in a short time?

Decision making is a sophisticated art and if we have the right ammunition it becomes easier to make smart decisions quickly. Be it what to eat and what to wear for work or which projects to prioritise, decision-making pervades our lives.

Thus the idea that decision making in itself can be a sophisticated art, may seem a bit unusual. However, studies have shown that most people, let alone organisations, could be far better at decision making contrary to their belief that they are.

Decision making in the banking context

Decision making is defined as, "...a study of identifying and choosing alternatives based on the values and preferences of the decision maker." One might want to not just identify as many possible alternatives but to choose the ones that have the highest probability of success or effectiveness and fits best with the goals.

Another interesting definition says, "...a process of reducing uncertainty with respect to the future." Both definitions have an underlying emphasis on information-gathering. But the fact remains, very few decisions are made with absolute certainty because complete knowledge about all alternatives is seldom possible.

In the case of financial institutions, decision-making becomes even more critical, given large quantities of data and incomplete knowledge and understanding of available alternatives. As much as it is critical, decision making is a complex task, because banks typically depend on three types of decision making, i.e. Operational, Tactical and Strategic.

All 3 deal with different dimensions of the bank's performance; from the routine / operational to strategic / long-term, thus making the process complex. Coherence between these different levels is essential and that too amidst increasing complexity of a bank's operations.

The opportunity cost of not having the right information at any or all of these levels can have significant impact on the bank's current and future performance.

Efficient support systems for effective decision making

Imagine you want to improve the performance of a decision maker. Will you leverage his strengths or compensate for his weaknesses? The best way would be to balance both because these strengths and weaknesses may interact. In many situations, a decision maker's weakness can undermine attempts to effectively play on his strengths.

The underlying data is analysed to identify a bank's internal strengths and weaknesses, and decisions are made to address external opportunities and threats.

We have seen that decision making is based on information that the decision maker is gathering. So, a good decision support system (DSS) must be able to take in the mass of information and extract the best alternatives that can help make optimal decisions.

For organizations with larger and ever changing data sets, a computer-based information system supports and hastens decision-making.

DSS serve the organization's management, operations, and planning functions and aid decision making, which may be rapidly changing and not easily specified in advance.

A properly designed DSS is an interactive software-based system - a business intelligence system that is intended to help decision makers compile useful information from a combination of raw data, documents, personal knowledge, or business models to identify and solve problems and make decisions.

At the core, the key benefits of DSS include the following:

• Speedy computation and problem-solving
• Improved communication and collaboration
• Increased productivity of group members
• Improved quality of decision making
• Improved flexibility of time and space of decision making
• Increased organizational control
• Help in analysing the prediction of risks
• Greater exploration and discovery on the part of the decision maker
• Generation of new evidence in support of a decision
• Creation of a competitive advantage over competition
• Revelation of new approaches to thinking about the problem
• Help in automating the overall managerial processes
• Ability to feed 'lessons learnt' back into the decision-making process

Such a long list of benefits renders DSS as an extremely useful tool in a bank's decision making process.

The critical role of DSS in banks

Retail banks in the Middle East, North America, Europe and Asia-Pacific are projected to spend up to $9 billion on business intelligence (BI) technology by 2012, according to latest industry reports by independent market analyst Datamonitor.

Given that strategic decision making is central to a bank's operations, the role of DSS naturally becomes crucial. With banking operations getting more complex and regulated, the use of DSS will only see an increase in the future. There is no other way banks can manage the vast data required to make timely and precise decisions, and to prevent unforeseen risks. If DSS is implemented properly in the banking sector, both effectiveness and efficiency of decision making will improve.

Straight2Bank, Standard Chartered's online business banking platform helps businesses reduce costs, improve decision-making clarity and drive growth. The Bank provides powerful and innovative electronic banking services with great flexibility that allows businesses to integrate their systems into the Bank's platform.

Take the case of Datscha, a DSS solution used by banks in Sweden. Datscha is a web-based service for performing analyses of the Swedish property market. Datscha provides detailed information of 350,000 commercial properties in Sweden, including information about the owner's facts, property size, taxation information and address. The functions include everything from finding information about a property or the rent in a specific municipality to the ability to perform advanced analyses of properties' market value. Whether users require a guideline or wish to conduct a full-scale assessment, Datscha's Property Analysis tool helps users conduct a full-scale assessment by providing the data into the cash flow mode. The key benefit for Swedish banks using Datscha has been better / faster analysis for better / faster decision making.

Another instance of a DSS solution benefiting banks was seen in the case of Ireland's second largest retail bank which effectively used DSS to manage its customer queries efficiently and subsequently curtailed operational costs.

Use Cases

Data Driven DSS

With 3000+ branches across 60 countries, ABN AMRO bank selected a Data Warehouse (DW) to build a platform for business decision support in Asia. The focus is on DSS for CRM, customer revenue analysis, and monitoring credit risk metrics. The DW will support the business development of ABN AMRO's consumer businesses in Asia. Their regional HQ in Hong Kong will be able to view the region's total business as well as the performance of each individual country's business, and each country will have a view of its own data.

Knowledge Driven DSS

Moody's Risk Management Services uses a knowledge-driven system to support the needs of commercial lending institutions. Over one-third of the top 100 commercial banks in the US and Canada along with some of the largest industrial and financial companies in the world use FAST (Financial Analysis Support Techniques) software for credit analysis.

Model Driven DSS

Standard Bank, one of the largest banks in South Africa, has operations in 17 countries. It was one of the early adopters of credit scoring and customer-level decisioning.

What to expect?

The opportunity cost of delayed and incorrect decisions can prove costly to a bank in terms of time wasted, money forgone and opportunities lost. Yet, there exists considerable ignorance of DSS amongst financial institutions, and more so, at the top of the pyramid, where most critical decisions are made.

Awareness and implementation of a robust DSS can safeguard an institution from potential crises bad decision making can trigger. Memories of the 2008 credit crisis are still fresh in our minds. What was it if not a case of bad decision making that was ultimately based on poor support systems available for making those decisions?

Of course, the behavioural part of greed cannot be ignored, but the fact remains that bad decisions at the heart of policy making were primary culprits in the crisis that shook the western world.

Questions:

1-In general, explains the benefits of Decision Support Systems (DSS) in banking sector. 

2-How ABN Amro Bank use the data driven of DSS in their bank?

3-Differentiate the use cases of DSS in the banking sector?