How to move easily from Excel to a ALM and/or Treasury System?

This article shares the easy steps to move from Excel to a Treasury and/or ALM System.

Many ALM and Treasury departments of smaller and medium-sized banks still use Excel to manage their ALM and/or treasury. There are many risks and disadvantages of using Excel; we listed 7 main ones in our previous blog post; read the full blog post here. We also made a follow-up blog post for sharing 7 reasons you should break up with Excel Spreadsheets and upgrade to a Treasury and/or ALM System. Read the full blog post here.

If you’re already convinced that you need to move from Excel to an ALM/Treasury system, continue reading. This blog post covers an easy 5 step approach to what to consider when making a move and being up and running in a few months.

Step 1: Scope of the project

It is essential to consider the scope of the system needed: for example, the risk surfaces the system must cover. The responsibilities of ALM and treasury might be wide-ranging, including, for example, Interest Rate Risk Management, Market Risk, Liquidity Management, and Risk Management. In addition to its strategic role within the bank, the solution might also need to support other functions, including financial planning, forecasting & analysis.

It’s efficient to have a shopping list for requirements. Discussing with many banks, we have outlined a system selection decision criteria checklist. The checklist should be viewed through both a current pain points lens, as well as, through a future requirements lens. See the complete list here.

Step 2: Data

Most Treasury and ALM systems tend to be very data heavy and data-intensive due to the nature and volume of a bank’s balance sheets.

Investing heavily in data management and integration with source systems helps with this. “It’s all about the data,” and the old “garbage in, garbage out” certainly applies to Treasury ALM systems. For this reason, you should invest heavily and cut no corners in designing key interfaces, such as the one(s) between the system and your core banking solution(s).

One of the key considerations is how automated vs. manual data management. An effective Treasury and ALM system must include powerful and intelligent data management. When data management and data reconciliation are automated, thenthe system is capable of handling transaction-level data. Rather than importing summary level whole balance sheet in one go, the system will import transaction level data as changes occur in the balance sheet. This means only new, modified, and matured transactions will be imported once the initial load of the balance sheet into the system has been done. For example, Markets and Treasury transactions must be imported and updated in real time. In contrast, banking book transactions are imported, e.g., once per day, as the banking book part of the balance sheet typically does not change so much during the day.

The data management should be largely automated and benefit from intelligent importing of source data. The software should allow the user to fully automate the flow between source data and reported data points. On a high level, your system needs to import data from other systems, for example, core banking systems and market data like Bloomberg. Further, MORS is highly adaptable in mapping between source data and data points, applying pragmatic logic to allow data import with minimum mandatory fields.

Step 3: Hosting

Critical data-driven questions are where, how, and by whom will the system be hosted? The system can be deployed  on-premise,  in the Bank’s own private cloud or as many of our recent customers have chosen, MORS hosted as Software as a Service (SaaS). Some banks prefer the system to be installed and run on their own servers to get complete control of their data, hardware, and software platforms. But then there are more indirect costs related to that. With private Cloud and Full SaaS solutions, you can benefit from lower IT support requirements and freeing up IT resources. Other benefits include elastic and faster cloud resources. Demand is growing continuously towards full SaaS.

Step 4: Preconfigured solution

Consider highly configurable systems that require less customisation or bespoke software development. This might sound like semantics, but a highly configurable system without the need to do further coding is, by most accounts, much cheaper and faster to implement. All data items and calculated results should be available for the simple and configurable building of reports or export to external applications. It’s easier to start using the solution if it comes with hundreds of pre-defined reports that can either be used for reporting or as the basis for creating custom reports.

Consider intuitive and easy-to-use solutions, which have been developed by ALM and Risk practitioners who have themselves worked in the ALM and Treasury functions of banks.  MORS for example was developed alongside the business at Handelsbanken.

You might even consider turnkey solutions, which are preconfigured. These solutions have pre-built templates, models, and reports. Banks can start immediately working towards a more profitable bank with no long and risky implementation project. With this kind of solution, environments are automatically provisioned and can be accessed in a couple of hours and full use within a few weeks.

Step 5:  The output of the system

We see a growing trend in the market where banks have more and more requirements to extract and export information and datasets from their Treasury and/or ALM systems. Fro example imports from Core Banking Systems, Trading systems such as TradeWeb, and Bloomberg. Exports to support the Enterprise Datawarehouse, Regulatory Requirements, General Ledger Accounting. For this reason, it’s essential to have a system with an information and dataset subscription feature. This feature allows the user to automate the extraction of information and datasets from the system in a user definable format and frequency,  All reports that the system generates for on-screen viewing should be set up to be automatically extracted and saved in a user-defined format and location. For example, the bank might want to develop its management information on key risk KPIs every day at 2 pm, which will be published in its BI tool. Such data workflow should be largely automated. Consider what KPIs are needed and require your future system to have an output of those.

Step 6:  Visualisation and Dashboards

Additionally, to the system’s output, you might be thinking, but what about charts and graphs? It might be the most exciting process to see what visualization tools the system offers. Dashboards, which deliver live data visualisations, are fantastic for at-a-glance views of your performance. Or to even use as a tool that can be taken into the Boardroom on a tablet, providing excellent key decision support. Great reporting and dashboard capabilities enable banks to identify relevant features that explain customer behavior, integrate them into the scenario and stress test analysis, and advance their balance sheet risk management capabilities.

The most advanced systems might even have multi-device browser-based analysis tools to present the results. The users of these systems are consumers of information rather than creators. Now more than ever, there’s a need to access vital information anywhere and on any device. As a reminder, ensure the browser version uses the same data as the primary system. So that the system works online and in real-time.


Most banks that still use Excel as a primary tool usually don’t have time to implement a new system or budget for it. The MORS team can help you to prepare a business case with you to learn about the savings in the long run and to help to make the transition happen by saving costs and time.

In some ways the easy decision is to carry on with manual processes and  Excel, but you should consider that there is a better way and it is quicker and less painless than you think. Book a free MORS web demo.

MORS Software has developed a guide for banks, when selecting a new Treasury Management System (TMS) or Asset Liability Management (ALM) system. The paper also outlines our approach to delivering and implementing Treasury ALM systems. Download: A guide to selecting a Treasury ALM system