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Data Security in Fintech Applications

Data Security in Fintech Applications
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7 min read

Financial transactions are often the target of hackers who want to make money quickly. Because of this, traditional banks have to follow strict rules about fintech cyber security. But fintech companies don’t have the same level of oversight as banks, so they often cut corners when it comes to security, even when they aren’t required to.

With the help of data, fintech may improve a company’s communication strategy, inspire the creation of brand-new goods and services, and teach it more about its consumers. Customers benefit, and fintechs may be able to make the most money from both their current customers and new ones.

Use Databases Efficiently

To store and use fintech data, your product should use databases attached to fintech security. Databases divide into two big types: relational and non-relational ones.

Relational Databases

A relational database organizes data into rows and columns by assigning a unique key to each row. Almost all relational databases have traditionally been rigid, hard-to-change systems that can’t store a lot of unstructured or messy data.

But relational DBMSs are used a lot and are necessary for keeping fintech data security, accurate records of transactions, historical data, and a lot of other things in businesses of all sizes.

Non-Relational Databases

In the mid-1990s, with the rise in popularity of the Internet, relational databases could no longer handle the flow of information required by users and the wide variety of data types that resulted from this development. This ищщіеув the development of non-relational databases.

Non-relational databases can quickly process data that is not well organized or is only weakly organized. They can also avoid SQL’s rigidity by switching from “strictly organized” storage to storage that is more flexible and still providing cyber security for fintech.

Which Type Database to Choose?

Relational and non-relational databases have their pros and cons. In relational databases, there is a limitation: each data element can only contain one attribute. In a non-relational table, each row can contain information about a customer and, at the same time, information about its counterparties, sales, and payment history. All of one customer’s data can be saved together in one convenient record in fintech app security solutions.

While a non-relational database has certain advantages when storing data, it also has a significant disadvantage: key-value repositories cannot “provide” relationships between items. This means that all customer data (name, address, payment history, etc.) will be stored as one record.

Customer excitement relies a lot on data security in fintech. In a relational model, this data would be structured to varying degrees, that is, stored in multiple tables, ensuring that there is no redundancy and that integrity is maintained. This means that the relational model has a built-in, robust way of providing business logic and reliability at the database engine level. For example, the use of a primary and foreign key allows payments to be shown in the appropriate customer account. This is why relational databases remain popular.

Every Microservice Needs Its Own Database

Remember this as a rule. Imagine that you are building an online banking app using the microservices architectural pattern. There is a vendor service that keeps track of what people spend, and there is a customer service that keeps track of who uses the app.

A microservice’s permanent data should be kept secret and only be accessible through its API for data security in finance industry. When it comes to services, all business is done inside the database. With a separate database for each service, it is easier to figure out who is in charge of what. For example, you could give each service its own unique database user ID and connect a few services databases through it.

Must-Have Fiscal Fields

Accounting depends on the Created DateTime and Updated DateTime variables, as well as the CreatedUser and UpdatedUser integers. If you don’t have them, your fintech solution won’t work. Basically, each transaction made with your fintech product must have the following financial fields: when it was created, when it was changed, who created it, and who changed it.

This method is based on a key idea in modern bookkeeping and accounting called “double entry”, which says that every financial transaction has an opposite sign in at least two accounts. In a double-entry accounting system, every credit must have a debit in the general ledger that is equal to and opposite of it.

The double-entry accounting system is based on the accounting equation, which says that the sum of the amounts posted to the accounts to the left of the equal sign must equal the amounts posted to the account(s) to the right of the equal sign. Each purchase or sale has its own debit and credit columns, and the totals of these columns must always be the same.

Some of the financial reports that can be made from the information in the general ledger are the trial balance, income statements, balance sheets, and statements of cash flows.

Never Delete Data

Finance transactions allow customers to view the state of balances, totals, and other important financial data on an entity in response to a financial action. Rules for cyber security in banks and fintech tell us that these records can’t be deleted.

Since it’s hard to tell which records are still in use, they all stay in the database. Your company may want to know about the previous actions or for data science in fintech so that it can make decisions that will lead it in a certain direction.

By adding a column to each table called “Date Time Removed,” you can “delete” rows without actually removing them from the database. Then, the “Date Time Removed” column would be used in your SQL or stored procedures. It also helps keep the referential integrity of data that is linked.

Also, remember to save all information about changes in a database that has the fields User and UserHistory. It helps in a lot of situations, i.e., when a user needs to restore access to an account or confirm the right to access these records.

Data Obfuscation

Obfuscating data means changing it so that unauthorized users (i.e., developers) can’t understand it, but computers and other authorized parties can still use it. This is done to ensure security of fintech from hacking.

Data fields are often hidden so that unauthorized people can’t get information that could hurt a company’s ability to compete. But the information must be useful enough for real test cycles to be run. Masking is more likely to be used on information that is not in the format of a company’s production system. That is, when information is needed to make applications, add to existing programs, or run different kinds of tests.

It is common practice to pull information from production systems to meet the data needs of these non-production situations. Still, this method does not have to be used only in test settings. Depending on the user’s access rights, some companies can dynamically hide data.

Payments Processing and Transactions Queue

A transaction is a group of related actions that are treated as a single logical unit when working with a database. All of the SQL statements that are part of a transaction can be either committed (added to the database) or rolled back (undone from the database).

Think of a financial database as a concept for a transaction. If a customer moves money from his savings account to his overdraft account, the statement will have four sections:

  • Debit SB a/c.
  • Credit OD a/c.
  • Record in Transaction Journal
  • End Transaction

Each transaction in a database must meet four conditions: it must be atomic, consistent, isolated, and persistent.

Atomic: A transaction is a logical unit of work that either includes all of its data changes or performs none of them.

Consistent: When a transaction is done, all of the information must be in the same state as when it started.

Isolated: Changes to data made by one transaction can’t affect the results of another transaction.

Persistent: After the transaction has been committed, the changes it made should be stable in the system.

As a result of a transaction, many SQL statements start to run. When a transaction starts, its atomicity is guaranteed. Once started, it can be committed to make changes permanent or rolled back to remove the changes and put the database back to how it was before.

When a transaction is committed, it is closed and the changes it made become permanent. When a transaction is rolled back, it stops, and any changes made are thrown away.

It is crucial to process payments immediately, when the user makes them. The thing is that payments are time sensitive: when your code calculates them, gives requests and awaits for answers, the payments processor may abort the transaction because the session exceeds. So you need to cut the time and use transaction queues.

Wrap Up

Companies may make security mistakes because they want to be “first to market,” but it’s also no secret that security must be important.

Finance software development companies must always be on the lookout for potential security threats, and they must have policies and procedures in place to make sure that new software and hardware is made in a safe way. Security is not something you should think about after everything else is taken care of. As soon as a plan is made to make an app or offer services online, security must be the top priority.

DashDevs is a top provider of secure solutions because it has helped companies in many different industries for many years. Our solutions protect data of Android, iOS, and hybrid mobile apps in a wide range of industries, from e-commerce to financial services, in a way that doesn’t slow down the apps. With real-time insights from threat analytics, you can stop data theft and keep up with regulations. Get in touch with us right away to create a fintech mobile app!

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