Do you remember the 2008 financial crisis? So many businesses went bankrupt, so many people lost their jobs; the entire world came to a halt. Why? Because people with bad credit were given loans that they couldn’t afford, and banks packaged these loans and sold them to investors. Consequently, it created a huge housing bubble, which eventually burst. It has been more than a decade since the crisis, but it is still fresh in our minds.
Now imagine how the world has changed today; with the technological advancements, the evolving customer expectations, and new financial products, not only the risks have amplified, but they have become more complex. Fortunately, technology has adapted as well. Financial institutions now have at their disposal advanced data management software and AI that can help them mitigate and manage their risks.
Data Management Software: A Strategic Asset for Financial Institutions
The world today is all about IoT, AI and Big Data. Every device, sensor, click of a customer and tap has the potential to generate insights. However, it all comes down to how effectively an organization can tap into this pool of data and fast it can convert it into insights. The need for speed has led to the rise of data management software that are designed to handle the high volume and variety of data.
The global data management software market stood at $89.4 billion in 2022 and it is expected to grow at CAGR of 12% from 2023 to 2030. Not a surprising figure given the pivotal role this software play for every organization.
When it comes to banks, data management software essentially helps increase data visibility. They help banks pull data from various sources and then put it together into a single picture. End-to-end data management software are equipped with advanced data extraction, integration, and analytics capabilities. On top of it, they are mostly automated, which can help banks enable real-time dashboards.
The Role of Data Management Software in a Bank
A bank deals with various risks daily, including foreign exchange risk, liquidity risk, credit risk and on top of it has to be on its toes to deal with money laundering issues. A data management software can help with all of these. However, before we go ahead and see how, lets understand how the software works:
Data Aggregation: At the first stage, the tool gathers data points from all diverse sources such as such as customer transactions, loan applications, credit scores, historical behavior, as well as external data sources like market trends, economic indicators, foreign exchange rates, regulatory changes.
Integration and Cleansing: Data management software then integrates data from these internal and external sources and normalize them into a consistent structure. This is crucial as different departments within a bank might use different systems, which is the major cause of data silos. Most software have inbuilt data quality features, which eliminate redundancy, inconsistencies, and errors, ensuring that the risk profile is based on accurate and up-to-date information.
Advanced Analytics: Data analytics has become the backbone of every industry, but it plays a super important role in finance. Banks can leverage analytical capabilities of a data management software to apply sophisticated analytics techniques, such as predictive modeling and machine learning algorithms, to identify hidden patterns, trends, and potential risks within the data. For example, the software could identify customers with a high likelihood of default based on historical loan repayment data and current economic indicators.
Combatting Money Laundering
One of the biggest challenges that banks deal with is money laundering. Statistics show that money laundered exceeds 2 to 5% of the global GDP, which is huge. Often the culprits succeed in tricking banks. Remember the Panama cases? However, data management software can help prevent money laundering. Here is how:
- Customer Due Diligence (CDD) and Enhanced Due Diligence (EDD): As mentioned earlier, data management software help banks create comprehensive profiles, based on personal details, transaction history, income sources, and more. These profiles allow banks to establish a baseline of expected behavior for each customer. In cases where customer activity deviates from this norm, it raises red flags.
- Transaction Monitoring: One of the best things that a data management software can do for a bank is real-time monitoring of transactions. Banks can set thresholds and rules for transaction monitoring and let the software analyze transaction patterns to identify unusual or suspicious activities. Most data management software is now equipped with AI algorithms that can detect anomalies within transactions and customer behavior. So, for example, a customer who frequently makes small transactions suddenly transfers a large sum to an offshore account could mean there is some unusual activity going on.
- Know Your Customer (KYC) Compliance: KYC regulations require banks to verify the identity of their customers. Although banks do their due diligence when a new customer opens a bank account, a data management software can make this process more robust and efficient. Banks can set automated identity verification procedures, which verify customer identities against multiple databases watchlists of known criminals, politically exposed persons (PEPs), and sanctioned entities.
Limiting Credit Risk
Credit risk was the primary reason behind 2008 financial crisis. While the market is more stringent and regulated after the crash, credit risk still poses a significant challenge for banks. Banks became more vulnerable to credit risk during the pandemic, which triggered unemployment across the nation.
A data management software can significantly help banks assess creditworthiness of applicants more effectively than traditional methods. For instance, when a customer applies for a loan, the software can instantly analyze their credit history, income, employment status, and other relevant factors. Based on this analysis, the system can provide a risk score and recommend whether to approve the loan, set specific terms, or decline the application. These recommendations are based on patterns and correlations identified in vast amounts of historical data and hence they are free from human bias.
Manage Foreign Exchange Risks
While foreign exchange risk is out of the bank’s control, proactively monitoring it can help a bank take corrective measures without delay. The real challenge however is managing data. Especially if there are data siloes, information about foreign currency exposures might be spread across different departments, such as treasury, trading, and operations. Moreover, departments might use different data sources or methodologies, resulting in conflicting risk assessments. With a data management software, a bank can have a central repository for all departments to access. Once the repository is ready, a bank can pair it with a visualization software to:
- Continuously monitor exchange rates for different currency pairs. The tool can notify when exchange rates reach certain thresholds, so the bank can review their decisions regarding currency conversions, hedging strategies, or trade adjustments.
- Analyze historical exchange rate trends and identify patterns and cyclical fluctuations, so a bank can forecast potential exchange rate movements beforehand.
- Aggregate and analyze a bank’s currency exposures across various transactions, loans, and investments and help a bank understand the overall risk exposure.
Conclusion
The financial sector is the backbone of an economy and so it must be vigilant when it comes to risk management. While the risks cannot be eliminated completely regardless of how strict the regulations become, they can be proactively managed through advanced technology. Everything relies on data today, but what matters is how that data is harnessed.
The role of these software solutions in enhancing the accuracy of risk assessment cannot be understated. By equipping banks with real-time insights, data management software empowers them to make agile decisions and keep up with emerging risks.
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