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Strategies for Financial, Tax, and Mortgage Software Systems
Banking’s technological infrastructure is vital for processing billions of transactions daily, reducing non-systematic risk, and producing an engaging customer experience.
A finance company’s chief concerns should be interest rate risk, liquidity risk, operations risk, reputation risk, and corporate compliance. Finance companies understand that reputational damage and improper risk management can be deadly. Risk intelligence proactively looks to increase the availability of information in the creation and use of tools, processes, and people for decision makers.
Artificial intelligence, machine learning, and deep learning are just some of the analytics tools that can identify and price underperforming securities as investment opportunities. Current and historical trends to forecast a company’s profit potential, as well as mitigate any non-systemic risk they may face are the foundations for an analytics team in the finance space.
With solid data governance in place, finance companies can deliver customer personalization that taps into a customer’s wants, desires, and needs that will be recognized and rewarded by all. By combining demographic data with transactional, website, social media, and sales and marketing data, deep and meaningful customer relationships can be built, resulting in strong customer loyalty and powerful social media advocacy.
Setting the stage for a streamlined, end-to-end business analytics process that mitigates risk.
Data governance provides protection to a company’s most valuable assets – its data. Data governance provides a common understanding of data, as well as improved data quality, a data map, a 360-degree view of each customer, and easy compliance with regulatory and government data guidelines, alleviating the potential of receiving costly fines.
Cyberattacks have tripled over the last decade and the financial services industry has been the most targeted. Cybersecurity has become a threat to financial stability. Besides the systematic risk involved in a massive online cyberattack, smaller attacks can be devastating to companies unable to access their financial accounts. Ensuring protection against cyberattacks is a difficult but necessary procedure.
Machine learning can spot credit card or transaction fraud in real-time. A proper risk management system can compare real-time transactions against these models and uncover suspicious activity. Once spotted, potential fraud can be shut down and appropriate staff alerted.
Analytics can limit any operational risks caused by failed or inadequate people, systems, processes, or external events. Cybersecurity risks, environmental risks, legal risks, market risks, and fraud can wreak havoc on a company’s performance. Operation risk is mitigated by implementing systems and processes that proactively check and verify information accuracy.
of the top banks utilize IBM Z mainframes.
2021 Heun, David. “The Security Risks Lurking for Banks Still Using Mainframes.”
SAP for Banking
Avaloq Banking Suite
Premier Bank Platform
Temenos Transact - Core Banking
Senior consultants with previous experience with these types of projects can set the stage for a well-framed engagement.
A focused session on your specific software applications, platforms, or projects. Typically this includes technical resources from both sides.