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Finance

19
Jan
Talkbot Basics  ·  Voice AI Technology
AI technology in finance: from concept to implementation
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

In a world of increasing data volume, managing spreadsheets and numbers is becoming more and more complex. Today’s finance organization needs a new set of tools to keep up with this fast-paced development. Enter AI technology. 

In this blog post, we present insights taken from Gartner’s webinar on the AI-Forward Finance Organization

Overload of financial data

Analysts predict that data volume will double to 181 zettabytes by 2025, which is a staggering amount. But what makes going through the world’s trove of digital data more challenging is its complexity, rather than its volume. Gartner foresees that data complexity will increase 4x in three years, twice the increase in volume. At the end of the day, it’s not just how much data we will get – it’s how much harder it will be to make sense of it all.

Many organizations today rely on spreadsheets for its operations, including financial functions. While these tools were powerful in their time, spreadsheets – even macro-enabled ones – are continuously being strained by high data volumes and complex data interconnectivities. It’s not uncommon to find broken links within spreadsheet models, or calculations that fail to present expected results due to technological limitations. The increase in data volume and complexity needs a smarter, more flexible solution.

Artificial Intelligence in finance

The rise of artificial intelligence (AI) in recent years has led to solutions that answer the data accessibility and navigability question. Enterprises are recognizing the power of AI and as of 2022, 59% of organizations surveyed by Gartner Research have started an AI initiative or adopted AI into their processes. And while this is a promising sign, it also speaks of risk to AI laggards. Businesses that fail to embrace AI technology to streamline processes will fall behind even faster in today’s accelerated market.

To embark on an AI journey, the key thing is to embrace change – to think differently and learn new things.

How to begin your AI journey as a finance organization

Start top-down – digital transformation, especially for finance-related activities, gets stronger buy-in when top management initiates the change
Understand that AI deployment is non-linear – adopt an agile working mindset and be willing to have more cyclical deployment patterns
Be open to experimentation – most organizations adopting AI take five iterations to get right things right or succeed

How to pitch a pivot to AI

Many leaders see the value of AI but need to get buy-in from upper management or other stakeholders. Here are some points that can help bring people into the fold and accelerate your company’s AI adoption.

Take the quick adoption route by buying software or subscribing to services. 
Position it as a proof-of-concept exercise where further adoption is dependent on results gained. Taking a packaged solution is one of the most frictionless ways for finance companies to dip their feet into the AI pond. For organizations that require debt collection or payment reminders, WIZ Talkbots are an easy way to introduce AI technology and quickly reap the benefits of AI-powered automation

Optimize human–machine collaboration.
Some of the pushback when it comes to AI stems from the fear of losing jobs. To overcome this, AI advocates should find ways where AI will complement human efforts. Best examples are process automations where humans are still needed for exception handling or complex cases. Remember, Humans are great at strategy and handling exceptions to rules, as well as seeing the big picture and drawing insights. Machines or AI are great at calculating, analyzing, executing processes, sending warnings at critical points, and enforcing rules or guidelines. Find the right balance between human and machine, and communicate how AI can empower humans to do more in less time or with less cost.

30% of businesses with advanced AI adoption report seeing better results than expected
– Faster implementation for new projects
– Greater business impact
– More process efficiencies
Significant AI adoption increased likelihood of financial benefit by 5x

Whether you’re a cutting-edge fintech or a well-established traditional, AI solutions should be in your future-proofing arsenal. And adopting a pre-built solution is one of the easiest ways to get started on your AI journey.

Looking to quickly deploy an AI solution into your debt collection operations? Our consultants would be happy to help
Contact us

29
Dec
Talkbot Basics  ·  Voice AI Technology
How AI can boost results in debt collection
Jennifer Zhang

Jennifer Zhang

CEO & Co-Founder

For many companies that need to conduct payment collections, resolving accounts that are past due is one of the trickiest parts of the business. Many of these customers are hard to contact, and once contacted, getting commitment to settle dues is another hurdle. Fortunately, conversational voice AI can help.

Harnessing the power of AI technology for debt collection

Artificial intelligence can be used in the debt collection process in a number of ways. First, AI can be used to automate manual processes like transcribing calls and categorizing customers based on their willingness to pay. These two tasks alone provide companies greater control over their debt collection processes because of the wealth of data that gets collected. Automatic transcription of calls transforms unstructured voice data into structured data. With it, enterprises can now do more detailed data analytics of their customer interactions. Those tasked with debt collection can gain a better understanding of risk behavior and improve collection tactics, moving from a reactive position into a more proactive one.

Loan provider gets more payment commitments with voice AI calls

This example features a finance company that provides loans and similar products to consumers. One of its biggest challenges is reaching accounts that are past due and making sure that commitment to payments are made. This often requires persistent outreach and multiple calls, which take a lot of time and effort for human agents. By switching to an AI-powered agent, the company was able to ramp up its call intensity proportionally to the account’s days past due.

call intensity 1-day, 2-day, and 3-day past due customers

With artificial intelligence, the company was able to call customers with overdue payments more consistently and with greater frequency. This resulted in obtaining “promise to pay” commitments from more customers – up to 60% for accounts that are three days past due.

Traditional bank gets clearer picture of consumer credit portfolio

In this second example, a large regional bank decided to deploy voice Talkbots for their credit collection operations. With each call, the AI engine records outcomes and tags customers accordingly.

The first layer of tags involve the call status – whether the customer was contacted or if the call failed to connect. Once the call connects and the Talkbot is able to speak with the customer, the AI engine then identifies whether or not the customer is willing to pay. If agreement is obtained, the AI agent then secures a payment date within a three-day window and records the response.

Having collections information structured in this manner enables the bank to see just how many of their delinquent accounts were willing to pay, and how soon. With this information, the bank is able to forecast its cashflows more accurately and update its collections strategy more proactively.

Fintech company gets record results in three-day collections campaign

This final example involves a non-traditional financial services provider that undertook a three-day call campaign for uncollected payments. Utilizing voice AI Talkbots, the fintech company was able to reach almost half of its customer database. Over 300,000 calls were made across a three-day period, obtaining payment commitments from two-thirds of customers that had overdue accounts. 

Of those that had committed to pay, over half promised to settle their dues within the day. Almost a fifth committed to pay the next day, bringing committed payments to 71% within a 48-hour window.

Getting ahead of back payments with artificial intelligence

AI powered much of the success of the above examples – artificial intelligence is what enabled companies to undertake the huge volume of calls to customers. And thanks to well-designed scripts, these companies were able to obtain payment commitments that previously eluded them. Results can be seen even in a short three-day campaign.
Are you looking to improve the outcomes of your debt collection activities? See how our voice AI Talkbots can help by booking a demo with us today.
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