How Much Machine Learning Is Useful?

Machine Learning (ML) helps in identifying various patterns in big data sets. By detecting relations between events and sequences, ML algorithms extract the relevant information hidden in raw data acquired from different sources. These patterns are hard to identify with human expertise since there exists a certain level of complexity, and the amount of work is enormous. Artificially intelligent systems can learn from a set of examples and perform predictions based on learning, which helps FinTech businesses to recognize potential opportunities ahead of time. Artificial Intelligence also utilizes biometric authentication technology to make customer verification procedures safe, reliable, and fast for Fintechs.

Machine Learning Applications in the Banking Industry

With the power of machine learning, Fintech companies operating in the banking sector can achieve better interest rates from ventures. According to research by Venture Tech on categories in Artificial Intelligence, machine learning technologies and applications dominate the finance sector by a fair margin. The fact that the banking and finance industry incorporates massive volumes of user data makes the problem challenging. Machine learning presents both a viable and reliable means to process and analyze the information through real-time algorithms with better turnaround times.

Optimized Loan Management 

There were days when issuing a loan from the bank took long hours of verifications and lengthy procedures. With advances in the recent past, peer-to-peer lending over the internet made it possible for consumers to borrow loans over the internet much faster. But recently, interest rates have also gone sky-high for P2P lending that forces conventional banks and digital lenders to improve the market situation without adding any potential risks. 

Machine learning beats conventional ways and means of loan accommodation by better assessing the borrower’s ability to pay back funds. This is possible by analyzing the user’s portfolio through a series of verification checks which involve machine learning techniques. A few factors which these algorithms take into account are social profiles, rent payments, or utility bills. 

Conventional Loan Grants

Banks and financial institutions have been using antiquated methods to grant loans to borrowers. Banks review loan applications based on the following information: credit history, cash flow statements if you are an organization, previous bank statements, and proof of bank security. Based on these items, the bank allotted a particular credit score to the individual requesting the loan. The review process becomes tedious and time-taking when performed by a human workforce. 

How does Artificial Intelligence Help?

AI-based algorithms assess the complex patterns involved in financial records to assign a risk score based on each parameter. An accurate risk score is computed by aggregating and analyzing data of thousands of users, which is then used as ground truth for further computations. Machine learning algorithms embedded in banking systems review the lender’s information based on pre-defined parameters, generate a risk score based on the analysis and approve the lender’s request if the score is below the threshold and vice versa. 

Benefits and Takeaways 

Credit scoring for loan grants is automated and easy to perform with intelligent algorithms and come with a couple of advantages which are:

• More borrowers are attracted towards banks and lending platforms with better loan approvals
• The overall risk of a fake lender is significantly mitigated, and an accurate credit score is assigned

Identity Theft Protection

Fintechs face many cybersecurity threats due to a large number of monetary operations involved in their business activities. Whether it is an already established enterprise or an emerging startup, cybercrime is always imminent. The aftermath of a data breach can cause irreparable damage to the financial organization that may take years to recover. According to the Identity Force, almost 14.4 million users were subjected to identity theft fraud in 2019, and the number is increasing with each passing day. 

Machine Learning algorithms can detect suspicious transactions and track customer behavior to safeguard banking entities from money laundering and terror financing activities. By learning about specific risk factors and unusual behavior in transactions, machine learning solutions deter fraudulent activities through business platforms and financial organizations by taking into account the historical data of a client. 

How Are These Solutions Helpful?

ML comes with a set of advantages when it comes to preventing online identity fraud in the banking sector, which is below:

• Effective defense against identity theft 
• Detect and rate the risk level of larger transactions which is hard to achieve with manual checks
• Pointing out hidden relations in data sets to track fraud
• Reduced verification measures and automatic detection with real-time processing

Comply with Financing Rules and Regulations

When operating in the banking industry, financial entities and organizations have to follow stringent compliance practices. These obligations are purpose-built to guarantee transparency in transactions and verify customer identity. They not only tend the bank or Fintech business to perform Anti Money Laundering checks but also enforce them to establish a proper means of user identity verification. Fintechs use regulatory technology (RegTech) to meet compliance requirements chartered by global watchdogs like the European Union, FinCen, and FATF. Machine learning aids financial organizations like banks to monitor transactions and identify anomalies using automated systems. 

How ML Helps in Compliance?

• Banking entities can comply with regulatory requirements better since supervisory expectations using intelligent solutions are met in a good fashion.
• Manual tasks performed by humans can be replaced with accurate procedures which help in meeting compliance
• Non-compliance costs can be significantly reduced and avoided by saving business expenses.

To sum it all up, artificial intelligence and machine learning help banks, fintech, and the finance sector in optimizing loan approvals, better risk assessment to take down identity theft, and follow international rules and regulations for financial compliance.

Well, it only takes a closer look to see just how wrong these assumptions are.

Much like any other human activity, the construction sector has been swept by the digital tide and pushed in the direction where we not only see the onslaught of new tools and construction methods but the very core of the construction process undergoes profound changes.

Let us take a look then at a couple of the tech trends that are currently shaping the construction sector and try to predict where these innovations will eventually lead.

3D printing finally picking up steam

We need to move this thing off the table first. The idea of 3D printing is, at this point, pretty old so citing it as some of the hot new trends doesn't seem entirely fair. But, these days, it finally seems that the tech has matured enough to fulfill its potential. At the moment, Palari Group is working on its first ever zero net energy 3D printed settlement in Coachella Valley. We can imagine even bigger projects taking place somewhere in the near future. The prospects of 3D printing can also have the biggest implications on the very nature of the construction process out all mentions we are going to cover here so keep an eye on it.

BIM keeps adding new dimensions

If you are not familiar with the term, BIM or Business Information Modeling represents the process of creating virtual representations of buildings under construction capable of displaying different aspects, dimensions, and stages of the project. At this stage, BIM models are capable of leveraging the costs of the project, sustainability of the finished product, and even safety and facility management. This whole plethora of different aspects and considerations packed into the same package also makes BIM models very powerful collaborative platforms bringing together different parties involved in the construction.

An unprecedented level of automation

The construction projects consist of dozens upon dozens of different activities that, when performed manually, considerably dragged down their speed and efficiency. These days, digital tech made most of these tasks much more streamlined. Even if we are talking about hands-on duties like equipment maintenance, you now have construction equipment maintenance software to help you out. What’s even better is that most of these tools can be partially or completely automated and even integrated into an automated, AI-run workflow. These things give the construction sector an incredible level of efficiency.

Robots, drones, and exoskeletons

Only a couple of years ago, these words could be found only in sci-fi movies. But, as we mentioned earlier, the tech is developing at a relentless pace and these technologies have become affordable and refined enough to become a fairly common thing at modern construction sites. All these tools do a great job at augmenting the human workforce, relieving the heavy worksite labor, and reducing the reliance on bigger, more expensive, and unsustainable tools. Since these tools also feature a great level of autonomy they allow workers to focus on higher-value tasks that require a greater level of skill and experience.

IoT overhauling the construction sites

The IoT tech has already completely turned around the services like on-demand taxi transportation. Now, a similar thing occurs in the construction sector as well. The modern construction sites are stacked with sensors and IoT-powered tools that can easily feed the workers with valuable environmental info, safety warnings, and work guidelines through various AR devices and wearables. The tools we have covered in the previous section, on the other hand, can substitute human labor in a range of activities which means the bulk of construction work can be performed remotely. This complexly changes the core of the industry.

AI becoming a main driving force

If you take a closer look at all the things we covered so far, you will see that all these breakthroughs, at least in some capacity, are being run by AI. Those five mentions are nowhere near the complete list of the cases where the construction industry becomes reliant on Artificial Intelligence and with the constant development of Machine Learning and higher-level analytics, that list will keep on expanding already including the mentions like financial planning, project design, safety assessments, etc. This development turns AI from one of the main contributors into the most essential pillar of the entire construction sector.

So, there you have it – the top six tech trends that are currently overhauling the construction sector and that will keep paving the way for further innovations in years to come. As we can see, AI and automation will keep playing an increasingly important role in the industry which means the human input will gradually become more limited to oversight, maintenance, and project handling. That doesn't have to be a bad thing since AI is traditionally better equipped of handling manual tasks. Human ingenuity can be saved for some more creative and responsible task.

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